Fisheries Oceanography of Yellowfin Tuna (Thunnus albacares) in the Tasman Sea

James Dell (BSc Hons.) Submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Quantitative Marine Science University of Tasmania November 2012

Cover illustration of yellowfin tuna, Thunnus albacares, by Roger Swainston © CSIRO Marine Research, Australia

Abstract

Abstract

Sustainable fishing is required to protect and maintain marine biodiversity and to ensure fisheries that are both economically viable and productive. Effective management of living marine resources requires well-informed decisions through an appreciation of past, present and future pressures. Understanding “why fish are caught where they are?” is the oldest question in fisheries research and is a central issue for the sustainable catch, management and conservation of marine resources. Here we look at the Australian longline fishery to better understand where yellowfin tuna (Thunnus albacares) are caught on the hooks set by the eastern tuna and billfish fishery (ETBF) in the Tasman Sea. We view the catch of this species in the context of the recent oceanography of the Tasman

Sea, sixty years of fishermen‟s knowledge and experience, a future climate scenario, declining stocks of other large pelagic predatory fish and an increasing demand in products derived from these species.

The physical environment directly influences the distribution, abundance, physiology and phenology of marine species. Relating species presence to physical ocean characteristics to determine habitat associations is fundamental to the management of marine species, however, direct observation of highly mobile animals in the open ocean, such as tunas and billfish, is challenging and expensive. As a result detailed data on habitat preferences using electronic tags has only been collected for the large iconic, valuable or endangered species. An alternative is to use commercial fishery catch data matched with historical ocean data to infer habitat associations. Using catch information from an Australian longline fishery and Bayesian hierarchical models we investigate the influence of environmental variables on the catch distribution of yellowfin tuna (Chapter

2). The focus was to understand the relative importance of space, time and ocean conditions on the catch of this pelagic predator. We found that pelagic regions with

i elevated eddy kinetic energy, a shallow surface mixed layer and relatively high concentrations of chlorophyll a are all associated with high yellowfin tuna catch in the

Tasman Sea. Time and space information, while important, were less informative than oceanic variables in explaining catch. An inspection of model prediction errors identified clumping of errors at margins of ocean features, such as eddies and frontal features, which indicate that these models could be improved by including representations of dynamic ocean processes which affect the catch of yellowfin tuna.

We use the same catch prediction model to consider where yellow fin tuna catches may occur in the context of a future climate scenario (Chapter 3). We used output from a global climate model (GCM) from the IPCC 2007 AR4 summit to produce predictions of surface ocean characteristics for the Tasman Sea in the 2060s. These data were used to initialize a biogeochemical model to create an ocean productivity product for the surface ocean that was equivalent to the chlorophyll a concentration as estimated by the ocean color SeaWiFS product. We use these products as inputs for the YFT catch prediction model to determine where YFT may be caught in the Tasman Sea in 2060s. We compare these predictions to those from the 1990s and the 2000s to show how the pattern of modelled YFT catches differ from those estimated by the model for the earlier time periods. Identifying possible shifts in the availability of YFT to commercial longlining over such long time period inform the construction of long-term goals upon which strategies for resource management; coastal infrastructure development and fleet management can be considered. This approach can also be applied at shorter time scales if biogeochemical downscaling is available.

Successful sustainable management of living marine resources can occur when the enhanced details of the resource, industry and market are thoughtfully integrated into the planning and implementation of management strategies. Engaging the fishing community

ii Abstract in the management process is a proven approach to the successful implementation of management strategies with sustainable outcomes. We report on a 2006 survey of the

ETBF which recorded the perspectives of the resource users and cooperative managers regarding the location and catch of YFT in the Tasman Sea (Chapter 4). We show that the fishing community hold varied perspectives on the most influential ocean characteristics with respect to YFT catch and show how perspectives relate to the fishing region. Further work collecting, analysing and incorporating the opinions and knowledge of the fishermen of the ETBF into habitat and catch models is recommended as a direction for future work.

Utilizing the qualitative information from fishers would minimise biases in the catch information, associated with the multispecies targeting and markets prices, and encourage better collaboration between fishermen, scientists and management for the sustainable future of the resource and fishing community of the east coast of Australia.

Overall, the work presented here show that YFT catches in the Tasman Sea can be partially explained by variation in the surface ocean environment. To achieve this goal, we used machine learning techniques to identify the most informative variables from the available ocean data and used a generalized linear model based on a hierarchical Bayesian framework to characterise the relationship between these variables and YFT catch. These techniques have not previously been used for this purpose in the Tasman Sea. The algorithms and model structures employed here provide a valid alternative to conventional habitat modelling techniques.

iii Declaration of Originality

This thesis contains no material which has been accepted for a degree or diploma by the

University or any other institution, except by way of background information and duly acknowledged in the thesis, and to the best of my knowledge and belief no material previously published or written by another person except where due acknowledgement is made in the text of the thesis, nor does this thesis contain any material that infringes copyright.

Statement of Ethical Conduct

The research associated with this thesis abides by the international and Australian codes on the inclusion of human interactions in research. This research was conducted subject to the approval and guidelines of the Human Research Ethics Committee (Tasmania : reference H0008690).

James Dell 29 November 2012

iv Abstract

Statement of publication and co-authorship

Publications produced as part of this thesis:

Dell, J., Wilcox, C.V. and Hobday, A.J. (2011) Estimation of yellowfin tuna (Thunnus albacares) habitat in waters adjacent to Australia‟s East Coast: making the most of commercial catch data. Fisheries Oceanography, 20, 383-396.

Dell, J., Hobday, A.J., Chamberlain, M., Matear, R. and Wilcox, C.V. (in prep.) Potential impacts of climate change on the distribution of longline catches of yellowfin tuna

(Thunnus albacares) in the Tasman Sea. Climate Science

Dell, J., Wilcox, C.V., Hobday, A.J. and Hindell M (in prep.) Fish Tales : can fishermen‟s expert knowledge improve models of tuna availability? (Fish and Fisheries)

Dell, J., Wilcox, C.V. and Hobday, A,J. (2007) 58th-Tuna-Conference-Proceedings.

Lake Arrowhead, California

Dell, J., Wilcox, C.V. and Hobday, A,J. (2007) Aiding the habitat characterization of pelagic predators using expert opinion. Annual meeting for the Society for Conservation

Biology. Port Elizabeth, Eastern Cape South Africa

v The following people and institutions contributed to the publication of the work undertaken as part of this thesis:

 Chris Wilcox (CSIRO Marine and Atmospheric Research), Alistair Hobday

(CSIRO Marine and Atmospheric Research) and Mark Hindell (Institute of Marine

and Antarctic Studies, University of Tasmania) assisted with guidance and

supervision in all aspects of the PhD and producing publishable quality

manuscripts

 Richard Matear and Matt Chamberlain (both of CSIRO Marine and Atmospheric

Research) provided assistance with the usage of data from dynamically

downscaled Global Circulation Models

 Robert Campbell (CSIRO Marine and Atmospheric Research) provided assistance

with the usage of commercial fisheries logbook information and guidance with the

management and suitable applications of these data

 Campbell Davies (CSIRO Marine and Atmospheric Research) provided guidance

on fisheries stakeholder interactions and resource management aspects of the

research and in producing publishable quality manuscripts.

We the undersigned agree with the above stated “proportion of work undertaken” for each of the above published (or in preparation) peer-reviewed manuscripts contributing to this thesis.

James Dell (Candidate)

Mark Hindell (Candidate‟s Supervisor) Tom Trull (Head of school)

vi Abstract

Acknowledgements

First of all, I must thank my supervisors Chris Wilcox, Alistair Hobday and Mark Hindell, whom I am deeply grateful for their expertise, broad vision, insight and patience. In particular, thanks to Alistair for encouraging me to undertake this challenging project.

Without his successful guidance and encouragement through my honors year, I would have never considered this path. His experience, extraordinary productivity and breath of engagement in the marine realm were an inspiration. Thanks to Chris for his unique vision and excitement for new approaches in understanding ecological processes, particularly when confronted with challenging data sets. Special thanks must be extended for his efforts and patience engaged when introducing (and re-introducing) me to realm of

Bayesian statistics and the power of machine learning. Finally, thanks to Mark for his sincere and cheerful dedication to and belief in his students. His academic experience, positivity, incisive guidance and can-do-attitude were of great aid and comfort at times when the going was particularly tough. I was well supported by both the joint

UTas/CSIRO Quantitative Marine Science program and CMAR‟s Wealth from Oceans

Flagship. These initiatives are helping to foster the future great achievements in marine science in Australia and beyond.

Thanks also to Richard Coleman, Tom Trull and Simon Wotherspoon for their perspectives, guidance and wisdom in their roles as QMS co-ordinators. Campbell Davies plated many roles in this saga, from mentor and motivator to humorist, surfing conspirator and close friend. I thank him specifically for his continued interest and belief in my project, particularly at the times when I had lost mine. His perspectives, positivity and wisdom were key to me continuing to push through towards the completion of this work and this thesis. I also benefitted from the tolerance and patience of Toby Patterson when seeking his knowledge on the nuances of R programming, and his perspectives on

vii statistics and rigorous scientific method. John Gunn provided five sage-like words early on, which I have continually reflected on and, in the end found to be true. Robert

Campbell provided great insight and guidance in relation to the dealing with the ETBF logbook information provided by fishermen via AFMA. He also reviewed an early iteration of the first data chapter and helped in guiding it towards the published form.

Beyond their kind encouragement and compassion in the corridor and at the kettle, Karen

Evans and Marinelle Basson provided incredibly thorough reviews of chapters and very helpful perspectives on the ecology of pelagic predators. Their critical input was instrumental in improving the rigor and clarity of some of the following chapters.

I must also thank the ex-corporeal “Pelagics group” at CSIRO Marine and Atmospheric

Research (CMAR) for being on of the most conscientious, humble, inspiring, fiercely intelligent and sincerely compassionate bunch of people I have ever had the pleasure to meet. Based purely on my experience within this group of people, furthering a career in science is an attractive option – even if only to associate with others whom share these same qualities. You are all amazing and surely some of the best global citizens anyone is likely to meet.

I am grateful to Mark Mangel and Don “Diego” Croll, who welcomed me to their labs, provided desks, imparted some wise words and perspectives and were just-all-round- inspirational while I visited UCSC briefly in 2006. I extend heartfelt thanks to Stacey

Buckelew for her friendship, inspiration, belief and tangential influences during my time as a graduate student. Straight lines are boring. Life and research should be a winding path. I finally JFDI and will be sure to remember your safety advice on all future expeditions.

viii Abstract

A warm thanks to the fellow inhabitants of the student dens at the Institute of

Marine and Antarctic Studies (UTAS) and CMAR, for sharing the joys and frustrations that only a PhD can evoke. Thanks also for the shared cakes and coffee runs that helped to break up the day and reminded me that I was once a social creature.

Special thanks are extended to the skippers of the ETBF who graciously volunteered their time and experience in answering questions that sought to dig out the invaluable information they have accumulated from many days and nights at sea.

I also wish to thank all those close and dear to me for their support and love during perhaps the hardest challenge I have faced. Mum thanks for the love and good vibes always. Dad, thanks for the inherited stubbornness and dedication. I am so very happy to share this achieve with you both.

And to the amazing Maia! For all the love, laughter and tolerance (massage, physio, acupuncture, culinary genius, domestic bliss and for finding the shack!). All your gorgeous grace, unending generosity, enduring patience and deep love kept me on track in the final stretch. Now we can finally go on holiday!

ix

Table of Contents

Abstract ...... i Declaration of Originality ...... iv Statement of Ethical Conduct ...... iv Statement of publication and co-authorship ...... v Acknowledgements ...... vii Table of Contents ...... x List of Figures ...... xii List of Tables ...... xv CHAPTER 1 ...... 1 General Introduction ...... 1 The Global Context for tuna and tuna fisheries ...... 1 The Tasman Sea and Eastern Australian Tuna and Billfish Fishery ...... 6 Spatial dynamics of YFT in the Tasman Sea ...... 12 CHAPTER 2 ...... 18 Abstract ...... 19 Introduction ...... 20 Methods ...... 24 Fisheries Data ...... 24 Ocean data ...... 24 Variable Importance using Random Forest Algorithm ...... 26 Hierarchical Modeling using Bayesian mixture models ...... 26 Estimation of model variance and errors ...... 27 Model structure and selection ...... 28 Residual analysis ...... 30 Results ...... 31 Distribution of longline fishing effort ...... 31 Preliminary variable selection ...... 31 Model selection ...... 35 Discussion ...... 41 CHAPTER 3 ...... 48 Introduction ...... 49 Models, data and Methods ...... 52 Catch and effort data...... 52 Modelling approaches ...... 52 Approach 1 : Predicted future YFT catch based on observations from recent past ... 55 Approach 2 : Predicted change in YFT catch from modelled climate scenarios ...... 57 Results ...... 59 Approach 1 : Hot and cold seasons from recent historical data: 2000 to 2010...... 59 YFT catch rate predictions for recent climate...... 60 Reviewing spatial ocean data from “simulated 2000s” and “simulated 2060s” npzdsGCM experiments...... 64 Approach 2 : Differences in YFT catch rates between the simulated 2000s and the simulated 2060s...... 66 Discussion ...... 70 CHAPTER 4 ...... 78 Fish Tales: can fisherman‟s expert knowledge improve models of tuna availability? .. 78 Introduction ...... 79 x

Methodology ...... 80 Data collection ...... 81 Collation of elicited information on ocean characteristics ...... 82 Multivariate Primer analyses...... 83 Assessing associations between fishers‟ interview responses and characteristics of their fishing strategy and success ...... 85 Results ...... 86 Interview Responses ...... 86 Multivariate analyses of interview responses ...... 87 Fishing locations and catch efficiency of interview cluster groups ...... 90 Multivariate analysis of fishermen‟s catch performance data ...... 92 Fishing locations of cluster groups based on catch performance metrics ...... 93 Multivariate analysis of fishermen‟s primary fishing zone ...... 93 Maps of skippers fishing zone groupings ...... 96 Maps of skipper grouped based on the region of their homeport ...... 97 Associations between fishers‟ interview responses and characteristics of their fishing strategy and success ...... 100 Discussion ...... 103 Why might Fishermen show a diverse range of perceptions on the specifics of YFT habitat? ...... 104 CHAPTER 5 ...... 110 Synthesis, future directions and conclusions ...... 110 Spatial models of large pelagic predators ...... 113 Potential impacts of climate extremes on future distributions ...... 115 Incorporating expert knowledge ...... 119 Future focus and directions ...... 122 Conclusions ...... 125 REFERENCES ...... 126 Appendix – ...... 143 Outline of the interview ...... 143 Compilation of selected interview responses ...... 156 Statistical details and results ...... 158

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List of Figures

Figure 1.1. Annual global effort of tuna fleets. Relative proportion of cathes are shown in the size and sector of each pie chart: skipjack (red), yellowfin (yellow), albacore (green) bigeye (dark blue), bluefin species (pale blue) (from Fonteneau 2004) ...... 5 Figure 1.2. Average annual catch of yellowfin tuna 2000-2010 (all gears). Catches are aggregated by 5 x 5 degree and legend relates to capture per square in tonnes. Map produced using FAO online atlas of tuna and billfish catches. http://www.fao.org/fishery/statistics/tuna-atlas/en ...... 6 Figure 1.3. Catches of major tuna species in the pacific ocean 1960 – 2010 ...... 7 Figure 1.4. Schematic of the main ocean currents off eastern Australia. Surface currents are shown in orange and subsurface currents are cyan. (Ridgway and Hill, 2009) ...... 8 Figure 1.5. Relative fishing intensity in the Eastern Tuna and Billfish Fishery, 2010. From ABARES Fisheries status reports (2011) Eastern Tuna and Billfish Fishery. In: Fishery status reports 2010: Status of fish stocks and fisheries managed by the Australian Government. (Wilson et al., 2011) ...... 12 Figure 1.6. Annual (line) and decadal (box) mean sea-surface temperature anomalies for the Australian region relative to the 1961-1990 average. The average value for the most recent ten year period is shown as a darker grey box. (Source: http://www.csiro.au/Outcomes/Climate/Understanding/State-of-the-Climate- 2012/Oceans.aspx) ...... 14 Figure 2.1. Total nominal effort (longline sets) for the Australian eastern tuna and billfish fleet from 1999 to 2004. Data are aggregated by quarter degree squares and plotted on a negative log scale to account for the large variability in the effort between squares. The box defines the area of focus in this study...... 30 Figure 2.2. Importance of variables as determined by the Random Forest Algorithm. Vertical bars represent the range in the results from 1000 model realizations...... 32 Figure 2.2. Importance of variables as determined by the Random Forest Algorithm. Vertical bars represent the range in the results from 1000 model realizations...... 33 Figure 2.3. Comparison of the coefficient sets used with the seasonal information included in the models. When season is used as a factor, summer is the base case (value 0) and all other seasons are relative to summer. The coefficients related to cyclic seasonal information were conditioned to fit a regular period...... 34 Figure 2.4. Posterior distributions of BIC for the leading models taken from MCMC. Curves to the left of the plot are the leading models. Colours are used to identify the curves (online version): green = model A (ov,c,y,t,Lat2,SW*EKE); blue = model B (ov,c,y,t,Lat2); red = model C (ov,l,c,y,t); black = model D (ov,l,c,y,t); purple= model E (ov,c,y); pink = model F (ov,l,f,y,t); orange = model G (ov,l,f,y); brown = model H (ov,l,c). Abbreviations explain covariate structure of models: ov = ocean variables (EKE, MLD SeaW), c = cyclic seasonal information, f = factor/corner contrast seasonal information, l = latitude, Lat^2 = second order latitude term, y = year, t = SST, SW*EKE = interaction term (SeaW * EKE)...... 37 Figure 2.5. Number of yellowfin tuna (YFT) caught per longline operation in the ETBF from 5000 randomly sampled operations used to parameterize the hierarchical models. Vertical line indicates the value of the mean error in the model prediction when compared to the observed number of YFT caught, as determined using the prediction diagnostic. .. 39 Figure 3.1. Effort data from the Australian Eastern Tuna and Billfish Fishery within the period used to parameterize the model used to predict YFT catches (2000-2004). Raw effort data are aggregated by quarter degree squares, legend scale relates to the total

xii

number of longline sets recorded within each square. Scale is capped at 700. The insert box defines the domain of the YFT catch prediction model...... 53 Figure 3.2. Comparison of the two approaches used to predict changes to YFT catch in the Tasman Sea as a result of climate change. The first approach used the observed extremes in SST to differentiate between cool and warm years within the recent decade (a). The second approach used an npzdsGCM to simulate the climate of two time periods, present and future, and determine the difference in the predicted catch of YFT (b)...... 54 Figure 3.3. Mean seasonal SST from within the Tasman Sea, 2000 to 2010, ranked from coolest to warmest. The horizontal lines show the decadal mean for each season. Red line = annual Summer mean; blue line = annual Autumn mean; black line = annual Winter mean; green line = annual Spring mean. The ultimate and penultimate years at the cool and warm ends of each series were used to predict YFT catch...... 59 Figure 3.4. Median predictions of catch for the (A) coolest and (B) hottest Autumn 2000 - 2010 and associated interquartile ranges (IQR) of predictions of the (C) coolest and (D) warmest Autumn. Meridional (lower) and latitudinal (right) summaries (at 0.2 degrees resolution) of the IQR predictions and ranges are presented with each figure. The summaries at the margins show the 2.5, 25, 50, 75 and 97.5 percentiles relevant to the corresponding increment of latitude or longitude...... 61 Figure 3.5. Median predictions of catch for the (A) coolest and (B) hottest Spring 2000 - 2010 and associated interquartile ranges (IQR) of predictions of the (C) coolest and (D) hottest Spring. Line plots at the margin of the figures are and meridional (lower) and latitudinal (right) summaries (at 0.2 degrees resolution) of the predictions and IQR are presented with each figure. The summaries at the margins show the 2.5, 25, 50, 75 and 97.5 percentiles relevant to the corresponding increment of latitude or longitude...... 62 Figure 3.7. Mean seasonal SST from npzdsGCM simulated present run (1998 - 2007) from within the Tasman Sea. Year runs are ranked from coolest to warmest. Red line = annual Summer means; blue line = annual Autumn means; black line = annual Winter means; green line = annual Spring means...... 65 Figure 3.8. Mean seasonal SST from npzdsGCM simulated future run (2064 - 2073) from within the Tasman Sea. Year runs are ranked from coolest to warmest. Red line = annual Summer means; blue line = annual Autumn means; black line = annual Winter means; green line = annual Spring means...... 66 Figure 3.9. Differences in mean predicted YFT catch rate for (A) Summer, (B) Autumn, (C) Winter and (D) Spring between those in the simulated 2000s and those in the simulated 2060s. The line graphs lower and right the margins of each difference plot show the changes of predicted catch rates pooled as Pooled meridional (lower) and latitudinal (right) summaries are presented with each difference plot...... 68 Figure 4.1. Catch composition of interviewed fishermen. Mean numbers of fish caught per set between 2000-2004...... 89 Figure 4.2. Multi-variate analyses of the interview responses given by selected ETBF fishermen. Branches of the Modified Gower‟s dendrogram that do not meet the SIMPROF test are coloured red. The furthest red branch of the dendrogram guided where a slice was made to define unique groups of fishermen. In each of the resulting plots, a number (1:23) identifies the set of skippers‟ responses and the coloured symbol refers to the cluster group assigned to each skipper in the Modified Gower resemblance and cluster analysis...... 89 Figure 4.3. Fishing locations of the participant ETBF fishermen, aggregated by the cluster groups determined by the Modified Gower resemblance of their responses to interview questions. The three panels refer to the cluster groups a(i), b(i) and c(i). The

xiii

grey cross and circle relates to the centre of nominal effort for the cluster group. The grey box approximates the area where 95% hooks were deployed...... 91 Figure 4.4. Multi-variate analyses of the catch performance metrics of selected ETBF fishermen. A Euclidian distance resemblance and cluster analysis. Branches of the Euclidian dendrogram that do not meet the SIMPROF test are coloured red. The furthest red branch of the dendrogram guided where a slice was made to define unique groups of fishermen. In each of the resulting plots, a number (1:23) identifies the set of skippers‟ responses and the coloured symbol refers to the cluster group assigned to each skipper in the Euclidian distance resemblance and cluster analysis. Note the absence of numerals 4 and 12, which corresponded to fishermen not active during 2000-2004...... 92 Figure 4.5. Fishing locations of the participant ETBF fishermen, aggregated by the cluster groups determined by the Euclidian distance resemblance of their catch performance metrics. The three panels refer to the cluster groups a(p), b(p) and c(p). The grey cross and circle relates to the centre of nominal effort for the cluster group. The grey box approximates the area where 95% of hooks were deployed...... 95 Figure 4.6. Multi-variate analyses of the metrics relating to the spatial distribution of effort of selected ETBF fishermen. A Euclidian distance resemblance matrix and corresponding cluster analysis. Branches of the Euclidian dendrogram that do not meet the SIMPROF test are coloured red. The furthest red branch of the dendrogram guided where a slice was made to define unique groups of fishermen. In each of the resulting plots, a number (1:23) identifies the set of skippers‟ responses and the coloured symbol refers to the cluster group assigned to each skipper in the Euclidian distance resemblance and cluster analysis. Note the absence of numerals 4 and 12 that corresponded to fishermen not active during 2000-2004...... 96 Figure 4.7. Fishing locations of the participant ETBF fishermen, aggregated by the cluster groups determined by the Euclidian distance resemblance of their primary fishing zone. The five panels refer to the cluster groups, a(fz) (yellow), b(fz) (lime green), c(fz) (green), d(fz) (light blue) and e(fz) (dark blue). The grey cross and circle relates to the centre of nominal effort for the cluster group. The grey box approximates the area where 95% of hooks were deployed...... 98 Figure 4.8. Fishing locations of participating ETBF fishermen grouped according to the region of their homeport. Red = a(hp), orange = b(hp), yellow = c(hp), lime green = d(hp)...... 99 Figure 4.9. Frequency plots displaying the count of skippers from each of the interview groups and how they were distributed in the groups derived from AFMA logbook data: homeport location, catch performance with respect to YFT and primary fishing zone. .. 101

xiv

List of Tables

Table 2.1. Pearson‟s correlation table of important variables in the study region. Correlation scores (r) are listed in the lower diagonal and probability values (p) are shown in the upper diagonal...... 34 Table 2.2. Competing model structures ranked by Bayesian Information Criterion (BIC). ov = ocean variables (EKE, MLD SeaW), c = cyclic seasonal information, f = factor/corner contrast seasonal information, l = latitude, Lat^2 = second order latitude term, y = year, t = SST, SW*EKE = interaction term (SeaW * EKE), dk = effective number of parameters; n is the number of samples drawn from both the base data and the test data in order to calculate the respective diagnostics. delta BIC = relative difference in BIC between ranked models. The grey section highlights models without EKE, MLD and SeaW...... 35 Table 2.3. Covariates and estimated coefficient values of the leading hierarchical models, as determined by BIC and the prediction diagnostic...... 39 Table 3.1. Median seasonal predicted catch rate of YFT, modelled from the surface oceanography from the coolest and hottest seasons from the decade 2000-2010...... 60 Table 3.2. Median seasonal predicted catch rate of YFT modelled from the surface oceanography simulated for 2000s npzdsGCM (control) and the 2060s npzdsGCM (future)...... 67 Table 4.1. Summary of spatial and fishing performance metrics used to characterize individual fishermen in the context of groupings arising from the multivariate analyses. 88 Table 4.2. Mean fishing performance characteristics within each Interview cluster group. Efficiency is a measure of fish caught per hooks deployed. Values in brackets are the standard errors related to each mean estimate...... 94 Table 4.3. Testing the independence of the groups with respect to the interview response groups ...... 102 Table 4.4. Association statistics ...... 102

xv

Chapter 1: General Introduction

CHAPTER 1 General Introduction

The Global Context for tuna and tuna fisheries

Understanding the factors that drive variation in the distribution of living marine resources is a basic requirement for making effective resource management decisions

(Costello, 2009). Investigating which physical and biological parameters best define the preferred habitat of a species helps to understand where and how that species fits within the functional ecology of the broader ecosystem in which they live. Once these basic parameters are understood for one region, these same associations can help to discover other regions where the species may exist. This simple process has been applied by fishermen and fisheries researchers for centuries and has been the basis of the global expansion in commercial fisheries worldwide (Hardy, 1965). However, a narrow understanding of the spatial ecology, dynamics and population ecology of commercially important species has contributed to rapid and/or excessive exploitation of these living marine resources (LMR), often to the detriment of the population, the dependent industries and the broader marine ecosystem (Hutchings and Myers, 1994, Hutchings,

2000, Jackson et al., 2001, Roberts, 2002, Ward and Myers, 2005b, Fromentin, 2009,

Orensanz et al., 1998, Hobday et al., 2001, Allen and Kirkwood, 1988, Field et al., 2009).

Currently most of the world‟s coastal marine fisheries are fully or over exploited and managed to varying degrees of success (Wijkstrom et al., 2004, Swartz et al., 2010).

The top 300 meters of the deep ocean, particularly in the tropical latitudes, are the home of worlds large tuna and billfish tuna fleets (Figure 1.1)(ISSF, 2011). There is substantial pressure on these highly migratory stocks, with many over exploited and some heavily depleted (Collette et al 2011). The demand for fish protein continues to increase along with the growth of the world population and increasing affluence in developing nations

1 Chapter 1: General Introduction

(Bell et al., 2009). Given the predictions that the changing climate will affect productivity of the ocean biome, and this growing demand for fish protein, many LMR will come under increasing pressure into the future. The FAO, and the global network of regional fisheries management organisations, emphasise that sustainable fishing practices need to be a primary focus for the effective management of the worlds LMR (Swan and Greboval,

2003, Wijkstrom et al., 2004). In order for sustainable practices to be effectively developed and implemented, increased understanding of the spatial dynamics and population dynamics of the resources are needed (Sibert and Hampton, 2003).

There has been increasing pressure world wide for proactive national and international responses to the combined pressures of overfishing and climate change on

LMR (Anticamara et al., 2011, Brown et al., 2012, Myers and Worm, 2003, Pauly et al.,

2005, Kirby et al., 2009). In response, some of the initiatives implemented in the south west Pacific over the last 20 years include: a range of input and output controls on domestic fisheries (for example, individual transferable quotas, removal of latent effort and restriction of licences and gear types); implementation of formal harvest strategies for fisheries consistent with principals of ecologically sustainable development (including a co-management model of participatory management) and focussing on multi-species and ecosystem based approaches (through developing regional marine plans and networks of marine protected areas) (Smith et al., 1999, Woodhams et al., 2010, Khan and Neis, 2010,

Smith et al., 2007).

2 Chapter 1: General Introduction

Box 1.1: Tuna are widely distributed in the world oceans

Actual observed pattern of species richness from Japanese pelagic longline data. Colors indicate the number of species in a standardized sample of 50 individuals (Worm et al., 2005)

Tuna species have evolved a suite of physiological adaptations to elevate the muscles responsible for locomotion above ambient temperature with complementary adaptations in respiratory and cardiovascular systems (Brill, 1996). These adaptations allow increased swim speeds, temperature tolerance and potential foraging range. The Thunnus genus is the most specialised and there are two main clades of tuna species, each has adapted to exploit different depths and latitudes. The Neothunnus clade comprises of the species found in warmer waters such as yellowfin (T. albacares) and longtail tuna (T. atlanticus). The other group is the coldwater or bluefin group, which includes bigeye (T. obesus), albacore (T. alahunga) and southern bluefin tunas (T. maccoyi) (Block and Stevens, 2001). The largest fishery for tunas is in the Western and Central Pacific Ocean (WCPO), where tropical, subtropical, surface and deep living tuna species are targeted with purse seine, pole and line and longline fishing methods. The Pew environmental trust estimates that, in recent years, over 50% of the worlds commercial tuna catch are captured in the WCPO, a figure that is supported by the International Seafood Sustainability Foundation (ISSF, 2011).

Throughout the global ocean, the sustainable capture and fisheries management of tuna and tuna-like species are the principal focus of five regional fisheries management organisations (RFMOs): the Inter-American Tropical Tuna Commission (IATTC), the

International Commission for the Conservation of Atlantic Tunas (ICCAT), the

Commission for the Conservation of Southern Bluefin Tuna (CCSBT), Indian Ocean

3 Chapter 1: General Introduction

Tuna Commission (IOTC) and the Western Central Pacific Fisheries Commission

(WCPFC). These organisations exist principally due to the highly, migratory nature

(populations classified as straddling stocks range over both the high seas and the exclusive economic zones of numerous nations) and international fleets that harvest them.

These stocks are highly valued and support a global market valued in excess of $US10 billion. These RFMOs were founded to ensure, through international cooperation and implementation of effective and sustainable management measures, to ensure the long- term conservation of highly migratory fish stocks in accordance with the 1982 United

Nations Convention on the Law of the Sea and the 1995 UN Fish Stocks Agreement.

Exploitation of tuna is a particular focus in many high seas regions as they are widely distributed (Box 1.1).

The largest fishery for tunas is in the Western and Central Pacific Ocean (WCPO), where tropical, subtropical, surface and deep living tuna species are targeted with purse seine, pole and line and longline fishing methods. The Pew environmental trust estimates that, in recent years, over 50% of the worlds commercial tuna catch are captured in the

WCPO, a figure that is supported by the International Seafood Sustainability Foundation

(ISSF, 2011)

Yellowfin tuna (YFT), after skipjack tuna (Katsuwanas pelamis), are the second most landed tuna by multi-national fishing fleets, both globally and in the Pacific Ocean

(Figure 1.1). Yellowfin tuna in the South Pacific are considered to be panmictic across the

WCPO and Southwest Pacific region. A majority of the population is located in the epipelagic zone in areas where water temperatures consistently exceeds 26 °C (Suzuki,

1994). These warmer water areas are mostly within the latitudes 20°N and 20°S. It is within these latitudes where the majority of spawning occurs at numerous times throughout the year. A moderate amount of the variability in the location and timing of

4 Chapter 1: General Introduction spawning activity and successful recruitment has been associated with location of the western Pacific warm pool (Lehodey et al., 2003) and with oceanographic characteristics in the northwest region of the equatorial zone (Langley et al., 2009a). The mechanism that drives the rate and numbers of recruits that move into the higher latitudes, such as the

Tasman Seas is still poorly understood (Hampton and Gunn, 1998, Gunn and Ward,

1994). While YFT are predominantly a tropical species, their specialized physiology allows them to utilize a broad geographical and temperature range, particularly the larger adults, but precludes them from regions where the water temperatures are consistently less than approximately 15° C (Box 1.2; Figure 1.2) (Brill, 1996, Sharp, 2001, Reygondeau et al., 2011).

Figure 1.1. Annual global effort of tuna fleets. Relative proportion of cathes are shown in the size and sector of each pie chart: skipjack (red), yellowfin (yellow), albacore (green) bigeye (dark blue), bluefin species (pale blue) (from Fonteneau 2004)

Yellowfin tuna is a comparatively fast growing, relatively young spawning tuna species, which means it is more resilient to fishing than the more temperate and deepwater species (Majkowski et al., 2007, Sibert et al., 2006, Fonteneau, 2004). The catch of YFT has steadily increased in the WCPO since the 1970s (100, 000 tonnes). The most recent estimate of catches from this region are in excess of 400, 000 tonnes, having peaked at over 500, 000 tonnes in 2008, which is above the maximum sustainable yield (MSY)

(Figure 1.3)(Langley et al., 2009b, Wilson et al., 2011, Reygondeau et al., 2011, Williams

5 Chapter 1: General Introduction and Terawasi, 2011). In this thesis, I focus on YFT in the eastern coast of Australia, where they are exploited by a multi-species longline fishery.

Figure 1.2. Average annual catch of yellowfin tuna 2000-2010 (all gears). Catches are aggregated by 5 x 5 degree and legend relates to capture per square in tonnes. Map produced using FAO online atlas of tuna and billfish catches. http://www.fao.org/fishery/statistics/tuna-atlas/en

The Tasman Sea and Eastern Australian Tuna and Billfish Fishery

The annual catch of YTF in Australia‟s Eastern Tuna and Billfish Fishery (ETBF) contributes less than one percent of the total annual catch in the WCPO. However, YFT represent a substantial and valuable proportion (25-35%) of the annual gross value product for the ETBF one of Australia‟s largest and most valuable fisheries. Over the last decade there has been a net economic loss from this fishery due to increasing fuel and gear prices, economic factors (unfavourable international exchange rates, shortage of skilled labour and relatively high cost of labour) and the increasing effort required locate the resource (Wilson et al., 2011) (Box 1.2). Much of the research funding that is drawn from the industry is directed towards the compilation and analysis of catch records collected for the Australian Fisheries Management Authority (AFMA) and analysed by the Australian Bureau of Agriculture and Resource Economics and Sciences (ABARES).

6 Chapter 1: General Introduction

These data are the basis of the stock assessments and harvest strategies and are used to produce the fisheries status reports (Wilson et al., 2011, Prince et al., 2010, Kolody et al.,

2010, Davies et al., 2008) for both the government and the WCPFC.

Figure 1.3. Catches of major tuna species in the pacific ocean 1960 – 2010 (Williams and Terawasi, 2011).

The ETBF extends across a spatially and temporally complex oceanographic system

(Baird et al., 2008, Ridgway and Hill, 2009). The primary inflow of water comes from the

South Equatorial Current, which flows west into Australia‟s continental shelf and separates into a northward and a southward current system, the Hiri and the East

Australian Current (EAC) respectively (Figure 1.4). Associated with the complex dynamics is a diverse biology. The key element to this system with respect to the concentration of large pelagic predators and their forage is the interface between the southward flowing EAC and the surrounding waters of the Tasman Sea to the east and the southern ocean to the south. As the EAC approaches its southern maximum, cyclonic and anti-cyclonic eddies are shed and move eastwards to form the Tasman front. At the boundaries between these water masses, there are vertical and horizontal gradients in temperature and current, which form frontal areas that can accumulate and support productive food webs, which attract both larger pelagic predators and fishermen.

7 Chapter 1: General Introduction

Box 1.2 Yellowfin Tuna (Thunnus albacares) Sources: (Wilson et al., 2011).

Figure 1.4. Schematic of the main ocean currents off eastern Australia. Surface currents are shown in orange and subsurface currents are cyan. (Ridgway and Hill, 2009)

From ABARES Fisheries status reports (2011) Eastern Tuna and Billfish Fishery. In: Fishery status reports 2010: Status of fish stocks and fisheries managed by the Australian Government. (Wilson et al., 2011)

8 Chapter 1: General Introduction

The catch composition of ETBF vessels varies depending on the market demand and the location and targeting practices of the fishermen, as well as the temporal and spatial variability of the target species. Over the history of the fishery focus has shifted between southern bluefin tuna (Thunnus maccoyii), yellowfin tuna (YFT; Thunnus albacares), big-eye tuna (Thunnus obesus), broadbill swordfish (Xiphidu gladius) and more recently to striped marlin (Tetraturus audax) and deep water albacore (Thunnus alulunga) (Box 1.2). Much of the fishing effort is concentrated within 300 nm of the coast and within the Australian Fishing Zone (AFZ) (Figure 1.5).

The changing focus of the commercial fishery and the changes in the domestic and international management of the straddling stocks contributes to the rich and complex history of the ETBF (Box 1.3). With the aim of controlling the effort expended in the

ETBF management measures have included the issuing and buying back of fishing permits (Box 1.3 a), implementation of quota systems and setting of total allowable catches (TACs) for species that required conservation measures due to declining catches or international agreements. Existing by-catch mitigation schemes include spatial and temporal area closures and policies related to the structure of fishing hardware, which have been put in place to limit the impact on southern bluefin tuna, surface orientated marlin species and seabirds. Further hook-based management policies related to spatial management within the ETBF are also being considered (Wilcox et al., 2011). Hence, a clearer understanding of the spatial dynamics of key target species, and by-catch species, is very important in determining which areas and times should be subject to management actions.

9 Chapter 1: General Introduction

Box 1.3 History of the Eastern Tuna and Billfish Fishery Sources:(Wilson et al., 2011, Campbell, 2008, Collette and Nauen, 1983, Langley et al., 2009b, Lehodey and Leroy, 1999, Pepperell, 2009, Shomura et al., 1991) a) b)

10 Chapter 1: General Introduction

Box 1.3- ( continued)

From ABARES Fisheries status reports (2011) Eastern Tuna and Billfish Fishery. In: Fishery status reports 2010: Status of fish stocks and fisheries managed by the Australian Government. (Wilson et al., 2011)

11 Chapter 1: General Introduction

Figure 1.5. Relative fishing intensity in the Eastern Tuna and Billfish Fishery, 2010. From ABARES Fisheries status reports (2011) Eastern Tuna and Billfish Fishery. In: Fishery status reports 2010: Status of fish stocks and fisheries managed by the Australian Government. (Wilson et al., 2011)

Spatial dynamics of YFT in the Tasman Sea

The observation of fast moving and widely ranging pelagic predators continues to present substantial challenges and without representative data, collected over broad spatial and comprehensive temporal scales, the development of a robust understanding of the spatial dynamics of these animals is not possible. Over the last 20 years the spatial ecology of primary target species of the ETBF has been investigated through infrequent

12 Chapter 1: General Introduction and relatively short duration research cruises, coupled with satellite remote sensing and electronic tagging studies (Young and Lyne, 1993, Young et al., 2001, Young et al., 1996,

Young et al., 2011, Young et al., 2010). The development of electronic tagging devices has enabled research into habitat preferences of large pelagic predators from an animals perspective (Gunn and Block, 2001, Gunn, 1999, Field et al., 2001, Charrassin et al.,

2008). The technology allows the continuous recording of water temperature, depth and in some cases feeding activity, while also providing an estimate of location and movement.

Within the ETBF there have been low numbers of these smart tags attached to various species which have resulted in short (< 1 month) to moderate (< 1 year) time series of location, movement and temperature preference (Evans et al., 2011, Evans et al., 2008,

Stevens et al., 2010, Patterson et al., 2008a).

The largest dataset on YFT in the Tasman Sea is the AFMA compiled logbook information from the ETBF. Each logbook entry details catch records of all species caught. YFT is caught on 80% of longline sets (Campbell and Young, 2012), even when targeting other species and is a major contributor to the gross value production of the

ETBF (Box 1.2 b) (Wilson et al., 2011). All hooks set by the commercial fleet pass through the epipelagic during setting and hauling of the longline, so even the fishing gear targeting the deeper living species can incidentally catch YFT. Thus, a majority of ETBF longline sets are sampling habitat that is suitable for YFT. The sampling effort is not random nor is conducted in a standard manner, so inferring YFT specific habitat from these records alone may introduce bias associated with the commercial, political and social motivations of the fishermen and the industry. However, when coupled with observed ocean data and modelled products from CSIROs Bluelink Ocean Data

Assimilation System (BODAS) (Oke et al., 2008), these data represent at least 15 years of daily location data for YFT and the other pelagic species complete with surface and

13 Chapter 1: General Introduction subsurface ocean conditions. These data present the opportunity to match high concentrations of actively feeding pelagic predators with characteristics and patterns of physical and biological processes of the upper ocean at the spatial and temporal scale of a longline operation. Previous investigations into YFT in the Tasman Sea have focused on population structure, abundance, movement and diet (Diplock, 1991, Caton and Ward,

1996, Young et al., 2001, Barratt et al., 2002, Williams, 2002, Kirby et al., 2003, Lowry et al., 2007, Young et al., 2011, Young et al., 2010). These previous investigations recognised the need to further elucidate the connection between the regional oceanography, ecological processes and the spatial dynamics of YFT in the Tasman Sea in order to better understand and manage the resource and commercial and recreational fisheries it supports. A greater understanding of the spatial ecology and dynamics of these valuable resources is necessary in the face of ever increasing global demand for living marine resources, the regional and international management pressure to ensure the sustainable harvest of wide ranging pelagic resources and the potential impacts of a changing climate on global marine ecosystems.

Figure 1.6. Annual (line) and decadal (box) mean sea-surface temperature anomalies for the Australian region relative to the 1961-1990 average. The average value for the most recent ten year period is shown as a darker grey box. (Source: http://www.csiro.au/Outcomes/Climate/Understanding/State-of-the-Climate- 2012/Oceans.aspx)

14 Chapter 1: General Introduction

Australian sea surface temperatures have been shown to be warming over the last

100 years with the first decade of the new millennium being the hottest decade on record

(Figure 1.6). It has also been identified as a region likely to experience rapid warming over the next century based on the projections of the current generation of Global

Circulation Models (GMCs). In light of these projections there is a pressing need explore the possible implications relating to the LMR upon which the ETBF depends. The output variables from GCMs for the marine environment are limited, both in number and in temporal and spatial scale. In order for these projections to be useful, the output must be downscaled. In the Australian region, two approaches have been used to achieve finer scale data: statistical and dynamic. The statistical downscaling is based on pattern matching (Ozclim: http://www.csiro.au/ozclim/home.do (Whetton et al., 2005)), and has limited ocean variables and does not resolve mesoscale ocean structures . A benefit of statistically downscaled output is that they are relatively easy to create and can be easily applied to a broad set of GCM ensembles. Dynamic downscaling uses regional climate models to calculate how the large-scale weather and ocean outputs from GCMs might drive the physical processes of the marine system. These possible impacts are determined through solving multiple equations relating to the transfer of heat and energy through multiple layers of the system. This makes output computationally expensive to produce.

However, they can produce variables relevant to the ecology of LMR at scales that are able to represent a realistic (and biologically relevant) mesoscale structure of the ocean at multiple depths. This is well suited to explore the ecological consequences of a shifting climates affect the boundary currents systems that support diverse communities of LMR .

Presently there is only one of these dynamically downscaled realisations of Australia‟s future marine current system, which presents a single, moderate simulation (IPCC CSIRO

Mk3.5 A1b scenario) of the future climate (Sun et al., 2011). The downscaling was

15 Chapter 1: General Introduction developed in order to explore possible modifications to the eastern and western boundary currents, the EAC and the Leeuwin current, due to climate change. Additional bio- physical fields were also calculated meaning that the output variables produced match those currently available from BODAS. The availability of such data enables habitat models, parameterized on contemporary bio-physical variables, to explore potential implications this climate scenario may have on future YFT distribution. Earlier work utilized multiple layers of temperature data from this regional downscaling to demonstrate future spatial management issues in the ETBF. Work contained in this thesis will go further and use temperature layers, productivity layers and output variables that characterise the horizontal movement and vertical structure of the surface waters. These variables will be used to explore possible future tuna distribution.

In addition to the few research cruises, much the knowledge about YFT habitat and movement in the Tasman Sea has been derived from commercial catch data. As mentioned above, there are limited electronic tagging data (~20 tags returned with short time series) available for this fishery/region and multi-scale spatial complexity means that tags alone may not resolve the issues. Hence, there is a need to develop estimation and modelling framework that incorporates various sources of information to help fill in the gaps of knowledge. The catch information fishermen record in AFMA logbooks is only a fraction of the data held within the commercial fishing community and comes with biases related to targeting, social and economic drivers. This expert knowledge is under utilized with respect to defining YFT distribution and feeding behaviour and could also be used to address the issues of bias that accompany the regular logbook data. Previously fishermen have been used as a data resource with respect to quantifying effort creep (Ward and

Hindmarsh, 2007) but not to provide their qualitative understanding of tuna oceanography. Pursuing actions that can maximise the information from fishermen also

16 Chapter 1: General Introduction provides the opportunity to enhance and cultivate a collaborative approach to resource management, which is becoming widely accepted as the most effective path to sustainable fisheries management.

The aim of this thesis was to explore the potential of using commercial catch data and fishermen‟s expertise to explain the present, and possible future, mesoscale distribution of a top order predator within a highly dynamic ocean region. This study has extended the value of catch records, produced alternative approaches, new perspectives and information on the mesoscale distribution on YFT with in the Tasman Sea. To this end, In Chapter 2 the commercial catch records and ocean data products were explored to better understand the spatial dynamics of YFT catches. Non-linear modelling techniques and hierarchical Bayesian General Linear Models (GLMs) were used to link fish to the ocean environment at spatial and temporal scales not previously attempted in the Tasman

Sea. In Chapter 3, the modelled associations between YFT catch and ocean characteristics determined in the previous chapter were used to investigate the possible future distribution of YFT. Chapter 4 investigates how Australian commercial longline fishermen perceive YFT habitat and whether there perceptions vary with respect to fishing success, fishing locations or social geography. These analyses present new research pathways that will inform and support the future sustainable management of the living marine pelagic resources of the Tasman Sea.

17 These chapters have been removed for copyright or proprietary reasons

CHAPTER 2 Estimation of yellowfin tuna habitat in waters adjacent to Australia’s East Coast: making the most of commercial catch data

Published as :

Dell, J., Wilcox, C. and Hobday, A.J. (2011) Estimation of yellowfin tuna (Thunnus albacares) habitat in waters adjacent to Australia‟s East Coast: making the most of commercial catch data. Fisheries Oceanography, 20, 383-396

http://dx.doi.org/ 10.1111/j.1365-2419.2011.00591.x

CHAPTER 3 Potential impacts of climate change on the distribution of longline catches of yellowfin tuna (Thunnus albacares) in the Tasman Sea

In prep. as:

James Dell, Alistair Hobday, Matt Chamberlain, Richard Matear, Chris Wilcox (2012) Potential impacts of climate change on the distribution of longline catches of yellowfin tuna (Thunnus albacares) in the Tasman Sea. Global Change Biology

CHAPTER 4 Fish Tales: can fisherman’s expert knowledge improve models of tuna availability?

In prep. as:

James Dell, Chris Wilcox, Alistair Hobday and Mark Hindell. Fish Tales : can fishermen‟s expert knowledge improve models of tuna availability? Fisheries Research

Chapter 5: Synthesis, future directions and conclusions

CHAPTER 5 Synthesis, future directions and conclusions

The ecological role of top fish predators in the open ocean is still relatively poorly understood (Heithaus et al., 2008, Block et al., 2010). This is a concern given the high and increasing levels of exploitation on the high seas, in particular in the South Pacific (Sibert et al., 2006). Tuna and Billfish species are the target of the most spatially extensive fishing effort in the Atlantic, Indian and Pacific Oceans. The catches of these fleets support a global industry worth over US$5 billion in exports annually, which represents

9% of the global fish trade (Grafton et al., 2009). A clearer definition of habitat use and distribution can contribute to a better understanding of the ecological importance of these species, while also informing development and refinement of assessment approaches and their sustainable harvest. This thesis contributes to the classification of locations within the eastern Australian fishing zone most likely to yield YFT catches. The possibility that these locations will change as the climate changes was also explored, with a suggestion that the main areas of catch are likely to shift south and east with the next 50 years, based on a moderate IPCC emission scenario and the effects of existing climatic variability on

YFT catch.

Large fast moving tuna and billfish species were historically considered “fully mixed” throughout the spatial range of each species (Hardy, 1965). Range predictions were based on catch records and the estimated physiological limits of each species with respect to ocean temperature, salinity and oxygen concentration (Broadhead and Barrett,

1964, Sund et al., 1981). Within these range limits, fish were assumed to have an ideal free distribution (Sund et al., 1981). Fisheries and biological oceanography of the open ocean have developed in alongside each other and the last 40 years have revealed: i) the

110 Chapter 5: Synthesis, future directions and conclusions upper 1000 m is not a homogeneous environment; and ii) the physical and biological characteristics are highly variable in space and time (deYoung et al., 2004, Sournia, 1994,

Santos, 2000, Bakun, 1996, Sharp, 2001). The distributions, movements and trophic interactions of pelagic species are driven by multi-scale temporal (diurnal, lunar, seasonal, decadal) (Lehodey et al., 2006, Chavez et al., 2003, Maury et al., 2001, Schaefer et al.,

2007, Evans et al., 2008, Lowry et al., 2007, Cayre and Marsac, 1993, Patterson et al.,

2008a) and spatial (from meters to 1000s of kilometers) processes (Belkin et al., 2009,

White et al., 2004, Fonteneau et al., 2008, Torres-Orozco et al., 2005, Waluda et al., 2001,

Palacios et al., 2006, Evans et al., 2011). Pelagic species (forage and predators) appear be associated with similar dynamic processes as they chase down concentrated resource patches that are intrinsically linked to meso-scale physical ocean features, such as eddys, rings, filaments, fronts, and up-wellings (Lehodey et al., 2006, Palacios et al., 2006, Acha et al., 2004, Belkin et al., 2009, Sournia, 1994, Lenoard et al., 2001, Polovina et al.,

2001).

Developing a comprehensive understanding and predictive ability of relative abundance, distribution and movement requires multi-scale spatial and temporal observations. Direct observation of animal location and behaviour in the context of meso- scale oceanography is expensive and usually confined to local investigations over relatively short time periods (Davis and Stanley, 2002, Bertrand et al., 2002, Marsac and

Cayré, 1998, Young et al., 2001, Young et al., 2010). Furthermore, these studies often focus on the most valuable or charismatic species. Automated observation of predators

(using telemetry) and remote characterisation of their environment (from satellite and modelling technologies) can also provide extensive time series of behaviour and movement (Bestley et al., 2008, Bestley et al., 2009, Evans et al., 2008, Field et al., 2001,

Hobday et al., 2009, Patterson et al., 2008a, Schaefer et al., 2007, Stevens et al., 2010).

111 Chapter 5: Synthesis, future directions and conclusions

However, imprecise specification of animal locations can make it difficult to match locations to the mesoscale ecological processes most important in shaping distributions or driving movements (Royer et al., 2005, Patterson et al., 2008b, Sumner et al., 2009). In the absence of comprehensive and precise fisheries independent data, catch information from fisheries presents the most comprehensive collection of observations (Chapter

2)(Dell et al., 2011). However, using catch data also introduces errors and biases, which need to be identified and considered when classifying ecological and stock status

(Carruthers et al., 2012).

In the context of finite funding, research must strive to develop effective and efficient approaches to understand the spatial dynamics of pelagic species. This requires integrative analysis of all available data and careful interpretation of results. The use of existing data series not necessarily collected for this specific purpose, such as fisheries logbooks, can make an important contribution, but requires careful consideration of potential biases that could be introduced as a result of the aims and methods of the original research goal. Issues include: varying levels of spatial resolution; incomplete or non-overlapping time series or time series that are of shorter duration of the known decadal scale drivers (e.g. ENSO). Furthermore, dependence on commercial fisheries logbook information introduces non-random distribution of survey effort, and possible reporting bias associated with issues of fishers wishing to maintain a competitive edge or other issues of commercial in-confidence (Beare et al., 2005, Bertrand et al., 2004, Branch et al., 2006).

Fishers, experienced commercial fishers in particular, accumulate enormous amounts of knowledge about the factors determining the distribution of their target species. Each develops their “model” of the system. This “model” is influenced by their own experience and that of others, conditioned by their observations of the system at a

112 Chapter 5: Synthesis, future directions and conclusions range of space and time scales (from annual to less than daily). Limited attention has been paid to the potential utility of this knowledge for developing better understanding of the spatial dynamics of pelagic species in the context of particular systems (e.g. EAC).

This thesis addressed three key issues:

. Methods for constructing and selecting parsimonious descriptive and

predictive models of habitat of wide ranging and valuable pelagic species;

. Extension of these models to examine potential impacts of current and future

climate regimes on the future distribution of this habitat, and

. The extent to which expert knowledge can be used to improve models based

on contemporary empirical observations

The case study used to examine these issues was YFT in the complex and dynamic oceanographic environment of the East Australian current in the Tasman Sea. The following synthesis examines the results of each of the previous chapters in the context for these themes. Specific and more general directions for future research are identified in the context of a changing climate impacting on the spatial dynamics of large pelagic predators and the consideration of the knowledge and experience of stakeholders in furthering our understanding of these important pelagic stocks and the management of the global fisheries that harvest them.

Spatial models of large pelagic predators

The observation and modelling of pelagic predator movement and behaviour in relation to ecological processes has evolved greatly in the last 30 years (Gunn and Block,

2001, Gunn et al., 1994, Schaefer et al., 2007, Field et al., 2001, Bestley et al., 2008,

Stevens et al., 2010). Characterization of key aspects of the physical and biological oceanography of the upper ocean is now possible due to a network of research vessels and autonomous vehicles which upload profile data from to satellites using the Argos system

113 Chapter 5: Synthesis, future directions and conclusions

(Roemmich et al., 2009, Johnson et al., 2009). The movement and behaviour of medium to large pelagic predators has also benefited from archival and PAT tags being attached to individual animals (Evans et al., 2011, Evans et al., 2008, Bestley et al., 2008, Field et al.,

2001). However, these data are invariably limited in terms of their spatial and temporal coverage.

There are large areas of the world's oceans where the matching of pelagic species with surface ocean characteristics remains the primary method for inferring habitat, ecological roles and potential distributions (Zainuddin et al., 2006, Valavanis et al., 2004,

Boyce et al., 2008). These approaches include matching known temperature preferences of a species with SST and temperature-at-depth climatologies (Hartog et al., 2011, Barratt et al., 2002). More complex models using additional variables from the upper ocean

(Murase et al., 2009), both correlative (Morris and Ball, 2006, Waluda et al., 2001, Chen et al., 2005) and mechanistic (Garcia et al., 2007, Lehodey et al., 1998, Lehodey et al.,

2008) have been used to predict species distribution, occurrence and abundance in space and time.

In Chapter two, a multistep analytical process was used to associate areas of high

YFT catches to characteristics of the upper ocean. Firstly, Random Forest machine learning techniques where used to select the most informative variables. Then these variables were incorporated into hierarchical Bayesian generalized linear models. Using these techniques we were able to illustrate that high YFT catches were most likely in locations where the surface ocean characteristics were: SST greater than 20 degrees C; relatively shallow MLD; increased current shear (EKE) and increased phytoplankton concentrations. These characteristics are consistent with those found in other productive frontal regions targeted by pelagic longline fisheries. Tuna species, such as YFT, albacore and skipjack tuna, have been shown to exploit feeding opportunities around fronts and

114 Chapter 5: Synthesis, future directions and conclusions mesoscale eddy features elsewhere in the Pacific, Indian, Atlantic oceans (Sugimoto and

Tameishi, 1992, Kirby et al., 2000, Fiedler and Bernard, 1987, Palacios et al., 2006,

Schick et al., 2004, Zainuddin et al., 2008, Seki et al., 2002). The findings of Chapter 2 show that these associations also evident in the Tasman Sea.

Because the modelling framework was general and flexible, further refinements or extensions can be made as more data becomes available or as qualitative information can be effectively incorporated. The Bayesian structure of the prediction model allows qualitative information, such as opinion or commonly perceived knowledge to be shaped into informative priors to further condition the model.

Potential impacts of climate extremes on future distributions

Changes in the distribution of marine resources are occurring globally (Last et al., 2011,

Ward and Myers, 2005b, Perry et al., 2005). Key drivers are a changing climate, the exploitation of LMR and anthropogenic modifications of the physical and chemical characteristics of the marine environment (Guinotte and Fabry, 2008, Rouyer et al., 2008,

Overland et al., 2010, Perry et al., 2010, Anticamara et al., 2011). The advanced physiology and broad diet of tuna species enable them to modify their spatial and behavioural choices in response to variations in the physical and biological oceanography

(Young et al., 2010, Block and Stevens, 2001, Brill, 1996). Regional changes in the temperature and circulation of the surface ocean will most likely effect the spawning and larval phases of these species, all of which spawn in the warm equatorial waters (>26°C)

(Suzuki, 1994, McPherson, 1991). The ontogeny, abundance and movement of the tropical tunas are also likely to be directly and indirectly influenced by a warming ocean, given that climatic variability has already been linked to the concentration of regional

115 Chapter 5: Synthesis, future directions and conclusions catches and spawning events (Langley et al., 2009a, Corbineau et al., 2008, Lehodey et al., 2006).

Two approaches were employed to explore some of the plausible effects of climate change on the future distribution of YFT in the Tasman Sea. The first approach used the observed variability of the surface ocean in the most recent decade (2001-2010) to demonstrate that the locations of high YFT catch shift between cool and warm climates.

The second approach used surface ocean data simulated to represent a present and future climate. The downscaled ocean model we used predicted that the southern end of the

EAC, its extension and the associated Tasman Front would all shift south, with the associated eddy field also shifting in location (Sun et al., 2011). These shifts in the EAC and its associated features are consistent with the changes observed and modelled over past decades (Cai et al., 2005, Hill et al., 2008, Ridgway and Hill, 2009, Ridgway, 2007).

The dynamically downscaled approach was favoured over the statistically downscaled primarily because YFT and other pelagic predators are known to be closely associated to frontal features (Fiedler and Bernard, 1987, Kirby et al., 2000). Dynamically downscaled models are a realistic representation of the regional dynamics, making it possible to explore spatial and temporal shifts in LMR at scales that are relevant to the behaviour of the target species and the commercial fishery that targets them. However, due to the expense and computing power required to produce the multi-layered and physically robust output, it is not currently feasible to compare across alternate future climate scenarios (Hobday and Lough, 2011). Statistical approaches provide this opportunity, but cannot produce the realistic mesoscale structure of boundary current systems, to which both tuna and fishing fleets orientate. Thus, dynamically downscaled models are most suited to the explorations undertaken in Chapter 3, which demonstrated that the potential changes in YFT catch will not involve a simple north to south shift.

116 Chapter 5: Synthesis, future directions and conclusions

This work suggests that the pseudo-regular mesoscale features, including cold and warm core eddies, which form at the EAC separation point (around 30° - 32° S), are likely to shift south and east and change in volume (Roughan, 2002, Suthers et al., 2011, Sun et al., 2011). In the present climate between 3 and 4 of these eddies are formed annually and can have lifetimes that exceed 1 year (Bowen et al., 2005). The downscaling predicts that the EAC extension will strengthen its flow by 35%, however, it is unknown whether this will increase the number and speed of these eddy features. These warm and cool core eddies are known to promote the aggregation of prey communities, and over time attract tuna and tuna like species (Young and Lyne, 1993, Young et al., 2011). The production and propagation of eddies are monitored by the commercial longline boats that operate in the central and southern ETBF. Information about the maturity of the pelagic communities present in each of these features is highly valued and is transferred selectively through the social network that exists between license holders, co-operative managers and fishermen. Future changes to the rate of formation, movement and productivity of these eddy structures will affect the fishing success of the longline fishers.

From the ETBFs perspective, continued observation and modelling of these systems is important and will allow potential changes to fishing success to be forecast so that effective and timely management decisions can be made.

There are some caveats of the dynamically downscaled model used in this work.

The simulation has no feedback of the ocean state to the atmosphere and it does not incorporate basin-scale ocean variability (Sun et al., 2011). Both these omissions mean that basin-scale and atmospherically linked climate phenomenon such as ENSO are not resolved. The model is based on the circulation of an ENSO neutral year.

This work represents the first step in preparing for the future challenges of managing YFT catch in the Australian fishing zone. The distribution of potential YFT

117 Chapter 5: Synthesis, future directions and conclusions catch will also depend on the how changing ocean conditions will affect the population dynamics within the ETBF and the greater WCPO. There is still much to understand on what controls the recruitment of YFT in this region and in the WCPO. Tuna recruitment processes in the WCPO has been associated with climate variability and the ENSO cycle affecting the thermal structure and productivity of the surface waters in the region, thereby shaping the areas where YFT prefer to spawn and feed (Lehodey et al., 2006,

Lehodey et al., 2008). For YFT in the north-west zone of the WCPO, regional oceanographic features have been shown to explain substantial amounts of variability in recruitment (Langley et al., 2009a). How these distant associations are related to the recruitment in the Coral and Tasman Seas is unknown. However, Lehodey et al. (2011) suggest that the increases in water temperature and oxygen concentration predicted for the

WCPO will result in a decrease in YFT biomass in the low latitudes and a progressive extension of spawning grounds towards the mid latitudes. This suggests greater spawning in the Coral Sea and waters at the northern boundary of the ETBF, with the likely outcome being a shift of YFT biomass from the WCPO to the south west Pacific. It should be noted that the same scenario suggests an ocean wide reduction of YFT biomass as the population shifts away from the warming waters of the WCPO (Lehodey et al.,

2011).

The exploration of future YFT distribution presented in Chapter 3 made the simplifying assumption of a status quo in regards to the population dynamics of YFT. The estimates of YFT distribution show only the areas of the Tasman Sea were the upper ocean characteristics are equivalent to those from locations where YFT have been caught

(Chapter 2). The true probability of capturing YFT in this region would also be a function of the impacts of fishing, both locally and in the WCPO to the north, and the proportion of the WCPO population of YFT recruited into the region. In addition, inter- and intra-

118 Chapter 5: Synthesis, future directions and conclusions species competitive, phenological and density-related effects would also interact with pressures from both fishing and a changing climate (Ottersen et al., 2010, Brander, 2010,

Griffiths et al., 2010). Making forward robust predictions of the interaction between climate change effects, population dynamics and the effect of fishing is currently not a realistic endeavour; given the broad uncertainties associated with each of interacting components. However, exploring how elements of the ocean systems may change based on the most likely future the climate trajectory is sensible (Perry et al., 2010). Modelling possible future distribution of LMR provides dependent industries with perspectives that can help them make informed decisions to ensure the their resilience to a rapidly changing climate (Miller et al., 2010).

Incorporating expert knowledge

There is a growing body of research that focuses on the value of incorporating qualitative data and information from fishermen to improve both the general understanding and specific ecology of LMR, particularly in localities where conventional data series may be lacking, too short or discontinuous. Expert knowledge of fishermen has informed qualitative and quantitative model structures used to locate of spawning areas, migration cues, schooling characteristics, define habitat, explore trophic relationships and refine stock assessments and management of LMR (Johannes et al., 2000, Mackinson and

Nottestad, 1998, Poizat and Baran, 1997, Saenz-Arroyo et al., 2005, Williams and Bax,

2007, Mackinson, 2000).

Fishing is explorative and adaptive skill that requires fishermen to constantly experiment with bait, equipment, weather and ocean characteristics and timing of their attempts to match the variability of marine resources. Accessing this information helps to broaden the scientific knowledge base and perspectives on the location, behaviour and origins of LMR (Stanley and Rice, 2007, Johannes et al., 2000). In the ETBF

119 Chapter 5: Synthesis, future directions and conclusions collaboration between fishers and scientists has occurred since the earliest satellite images of SST were made available. Fishermen also assist in vessel-based research and continue to be engaged in telemetry and otolith research programs, transporting scientists and retrieving samples and tags (Young and Lyne, 1993, Young et al., 2010, Evans et al.,

2008, Farley et al., 2006, Farley et al., 2011). However, while these roles are drawing on the experience and knowledge of fishermen, they do not directly access the perspectives, opinions and qualitative data that each fishermen accumulates from their own “research and experimentation” in identifying high value habitat (Stanley and Rice, 2007).

Fishermen are the apex predator in this system, and we are able to ask them what they are doing and how they are responding to cues that they observe, with or without placing telemetry devices on them. By digging deeper into the knowledge held by fishermen, beyond the information recorded in management logbooks, we may begin to fill in gaps with respect to the location, and the ecology of pelagic predators they target with respect to the observable characteristics of the marine system. This survey of the

ETBF fishermen indicated that there are fishermen within the fleet that keep highly detailed records of each longline operation. These data could further refine the scale of

YFT observations, particularly compared to spatial errors that accompany logbook data and electronic tags. Chapter 4 shows that there is spatial, social and skill-related variability in the way fishermen, these eloquent “apex predators”, view valuable habitat in the ETBF. This is very important to consider if-and-when fishermen‟s expert knowledge is given greater weight in the scientific investigation and modelling of the pelagic ecosystem of the Tasman Sea (Davis and Wagner, 2003). The need to identify and engage the regional specialists, by forming a better dialogue between fishermen and scientists are the key messages from Chapter 4.

120 Chapter 5: Synthesis, future directions and conclusions

There are challenges in engaging in collaborative relationship with the fishermen of the ETBF. The free exchange of information between fishermen and science risks undermining the management process for the fishery, given the possibility of conflicting motivations (Branch et al., 2006). Conventional sociological studies are expensive to conduct, in respect to both time and money; two resources that have not been abundant in the EBTF in recent years (Wilson et al., 2011). In the context of limited resources for science and management, prioritizing social research over “hard” science is contentious.

However, greater collaborative research, if not management, has been shown to be effective in other marine systems (Haggan et al., 2007, Grafton et al., 2009, Gutierrez et al., 2011). Thus, investing some resources into social oceanography can be a worthwhile investment in the sustainable management of LMR (Jacques, 2010).

Time and resource constraints restricted deeper exploration of the wealth of knowledge held in the collective minds and records of the ETBF fishermen. However, the information that was extracted in the short, single visit interview series was substantial.

Less that one third of the interview questions asked of the fishermen were used in the

Chapter 4 analyses. Each fishermen was only interview once. The semi-structured interview approach, while effective at reducing bias associated with directed interviews, was designed as a first stage of an interview series for each interviewee, where follow up interviews would fill in the gaps, clarify and check information provided in previous interview sessions. Best practice social research suggests that interviews of this type should involve a series of encounters with interviewees so that a common language can be defined, and parameters of the research made clear (Ames, 2007, Hay, 2000, Pollock et al., 2007). This helps record sufficient detail and refined responses from each interviewee in a format that is suitable to compare across all the respondents. Interview techniques and strategies were rapidly developed in the field. My interview skills improved throughout

121 Chapter 5: Synthesis, future directions and conclusions the survey period, thus it can be assumed that there was some small level of measurement bias between the first fishers interviewed and the last. However, given the individual variability in language, experience, fishing skill, perception of management and science, financial and political motivations between the fishermen interviewed, the bias associated with my developing skill as an interviewer was of little influence on the flow of information extracted. The interviews were conducted during a time of great uncertainty with respect to the social and political context of the ETBF. The fishery was undergoing a substantial restructure; with the removal of latent licenses and a reduction in the size of the fleet were being negotiated with the stakeholders (Box 1.3). The fishery was shifting from a state of overcapacity and essentially unregulated and intense competition to one of sustainable effort with Individual Transferable Quotas. This period of negotiation was no doubt a stressful time for many stakeholders and their willingness to openly share their time, information, expert knowledge and opinions may have been effected. These issues are highlighted here to demonstrate that a great deal more information on fishers‟ perception of YFT habitat, and their interaction within it, remains to be distilled from the interviews and extracted from those remaining in the fishery and, perhaps more so, those that have left the industry.

Future focus and directions

Improving spatial and temporal scale of reported fisheries data

The parameterisation of the models of YFT catch developed in Chapter 2 would have benefitted greatly from more complete fisheries logbook information. A majority of records had minimal information to characterise each longline operation, with only a single date and time and a single location, rather than start and end times and locations for both setting and hauling events. These data are useful to accurately determine the volume

122 Chapter 5: Synthesis, future directions and conclusions of the upper ocean that each longline samples, by taking into account of drift and shoaling

(Dunn et al., 2008, Bach et al., 2009, Lee et al., 2005). There are also many other fields on the AFMA designed logbooks that were left blank in the majority of instances.

Information relating to bait types, targeting and setting methods would have been very useful to identify which of the longline observations were more specific to YFT. Longline operations which occurred on vessels with an AFMA observer onboard have far more comprehensive data recorded for each set, but with only 5% observer coverage during the study period, there was insufficient numbers of observation and spatial coverage to answer the questions posed here. Increasing the numbers of independently observed longline catches may be the best way to gather the data required to better characterise the environment where YFT are most often present and caught on longlines.

Encouraging greater collaboration between longline skippers and fisheries scientists, through the exchange of knowledge for mutual benefit, would also be a worthwhile strategy in achieving the return of better quality logbook data. With the shift from an over-capitalized fishery with too many competing licence holders to a individual transferrable quota system, that rewards sustainable fishing practices may help to foster more convivial and productive relationships between fishermen and scientist for the benefit of the industry, the commercial stock and the environment. Automating logbook entries and/or replacing the tedium of paper based data recording with video, vocal and computer-aided data gathering methods would also be worthwhile investments towards improving the quality of fisheries dependent data.

Including more variables that directly influence the behaviour and movement of pelagic predators

Developing or improving methods to effectively characterise the relationships between primary and secondary production, accumulation of forage species and

123 Chapter 5: Synthesis, future directions and conclusions concentration of pelagic predators will lead to better estimates of the distribution and abundance of pelagic predators. Developing research in the areas of pelagic telemetry

(Bestley et al., 2008, Bestley et al., 2009), trophic investigations (Young et al., 2010,

Potier et al., 2007, Menard et al., 2007) and acoustic sampling of forage fields (Kloser et al., 2009) represent progress to this end. Findings from this research, and the broader perspectives, can be combined with the longer continuous series of catch and remotely sensed data. to update the variables and model selection from this study and potential explore the apparently non-linear relationship between dynamic surface features (EKE,

MLD) and chlorophyll a.

Broader scale ocean and atmospheric variables

Examining the potential role of basin and decadal scale processes, such as the interplay of oceanic Rossby waves and the ENSO cycle, on the rate of formation and periodicity of the dynamic meso-scale features that appear connected to the concentration of pelagic predators within the region (Holbrook et al., 2011, Young et al., 2001, Young et al., 2011). Similar connections between pelagic resources and these larger scale ocean features have been recognized in other marine systems and similar work in the Tasman

Sea would likely yield valuable perspectives on the variability of tuna catch in the region

(Palacios et al., 2006, Torres-Orozco et al., 2005, White et al., 2004). Incorporation of basin scale ocean variability, at decadal and inter-decadal time scales, will be essential to improving the utility of models to explore the future distributions of pelagic fish populations. However, the underlying physical mechanisms are still the focus of much research and predictive capacity is very limited beyond the very short-term (Lin, 2007,

Brown et al., (submitted), Brown et al., 2011b). It would be relatively straightforward to incorporate these types of inter-annual and decadal influences the habitat modelling framework developed here and, in doing so, provide the basis for integrated estimation

124 Chapter 5: Synthesis, future directions and conclusions and prediction of pelagic habitats of large pelagic predators, such as yellowfin tuna, across spatial and temporal scales of seasons to decades and kilometres to ocean basins.

Conclusions

1. YFT catches in the Tasman Sea are associated with meso-scale features. Most probable catch locations can be predicted using remotely sensed and modelled ocean data products: MLD, EKE, SST and primary productivity. 2. The distribution of YFT catches in the Tasman Sea is likely to change as a result of a changing climate. The future projections used here suggest that there will be a southerly shift in the areas with the greatest probability of YFT catch. 3. The perspectives surrendered by commercial fishermen on the biological oceanography of YFT vary depending on where they fish, their social affiliation and communication networks 4. Future research into spatial dynamics of YFT and its application to the sustainable management of the resource would benefit greatly from an improved dialogue and cooperation between fishermen and research organizations.

125 References

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142 Appendix

Appendix –

Outline of the interview

Fishing experience Vessel history and operation Chart based questions Gear and setting techniques Annual planning

Seasonal planning [Spring, Summer, Autumn, Winter] Trip planning Planning a set Discussion and final questions

143 Appendix

Distribution, movement and abundance of an east coast pelagic species and the effect of the variation in the East Australian Current James Dell - Primary Researcher, Phd candidate CSIRO/UTas Quantitative Marine Science Program [email protected] Mb 0401 334 177

Fishing experience, vessel history and operation

1. How long have you been longlining [for yellowfin]? (As crew/as Skipper)

2. How were you introduced to longlining?

3. When longlining, which species do you target the most often?

4. Where is your home port?

5. Which ports have you fished out of when longlining for YFT?

6. Which vessels have you worked on in since 1996? (As crew/as Skipper) [years on each vessel] [keeping in mind this information is totally confidential, and the information you provide will be recorded as “anonymous skipper x, y or z”. ]

Year Vessels Ports Summer Winter 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

7. Which region of the ETBF fishing grounds do you feel you know the best? (mark on Bathymetry chart Note chart to be of larger magnification show the fishers 3 regions – one per page North [ Cairns to South port] Central [Southport to Sydney] and South [Sydney to Eden] ) Make these ASAP

8. When fishing, do you think about how YFT react to; a. Your gear (how it is set, modifications etc) Yes/no b. The EAC Yes/no c. Local water conditions Yes/no d. Local weather Yes/no e. Other life in the ocean Yes/no (explain) f. Other Boats Yes/no g. A combination of (a-f) Yes/no (explain)

144 Appendix

9. Do you use satellite maps/products (like SST charts) and other web-based information to help locate the best fishing ground, a. Yes/no

b. which ones do you use,

c. what features do you look for,

d. how often will you use them and,

e. Of all the factors you consider, how much influence do these charts have on choosing good water you will place a set? [use the following as a guide] i. 0% - ignore it – I fish where I want ii. 1-20% - I try and fish away from everyone else and find my own water iii. 21-40% - Take a bit of notice iv. 41-60% - Half the time I‟ll use the Charts to choose water I want to fish v. 61-80% - More often than not I‟ll use SST charts to choose the water I want to fish vi. 81-99% - It‟s probably one of the best ways to find good water vii. 100% - I always base my on SST charts

10. What Sea Surface Temperature (SST) do you think is optimum for finding YFT?

11. What is the Coldest SST that you can find YFT?

12. What is the Warmest SST that you can find YFT?

13. Does the sea bottom (bathymetry) affect where you choose to fish? a. Yes/no b. What do you look for? c. How do you look for it? (charts/gps/sounder)

14. Is there any other (oceanographic) information about the ocean conditions that you would like to have access to? a. Yes/no b. What would you like to have access to?

Chart Based Questions Still in development; basic concepts outlined below.

15. Ask fishers “Which regions ETBF (Eastern Tuna and Billfish Fishing zone) do you know the best”; from a selection of 5 regions; South, Central and North NSW, Queensland Coast and offshore (East of 155 degree E)

145 Appendix

16. Second question asks the fisher to interpret a Sea Surface Temperature (SST) image from their region of expertise Fishers will be asked to mark the features that have a better than 50% chance of being a good set. *

17. From the areas they have selected they will be asked to rank the 3 best ¼ degree squares. and then explain why

18. When fishing in other regions, do you look for the same features? a. Yes/no b. If no why not.

19. With their fishing history in mind, fishers will be asked to interpret the charts from the other regions where they have fished. Fishers will be asked to mark the features that have a better than 50% chance of being a good set. *

<<*These questions will be presented using a laptop, using images from a Matlab based mapping program, so that the fisher can change the temperature scale of the image to suit their needs. Fishers will be presented with SST images from past seasons. Selection of days to be presented to the fishers will be based on the quantitative analysis of the catch record. Images will be taken from a day in the past where the catch record showed a wide spread effort and a variable catch.>>

20. When targeting yellowfin, are there specific gear configurations that work better than others? a. Yes/no b. Can you give some examples? (ie length and depth of set and number of hooks, basket configuration, bait type , fixed or sliding gear). [give to fishers to fill out]

21. Why do you use this gear configuration? What gear configuration do you think would work better and why?

146 Appendix

22. At what time of the day or night do you prefer to set your longline when targeting YFT?

If dependent on the Moon: New Moon: First Quarter: Full Moon: Last Quarter:

23. What was the soak time for a typical tuna set ? Does this change a lot between species and from set to set (season to season; year to year)?

If dependent on the Moon: New Moon: First Quarter: Full Moon: Last Quarter:

24. Do you try and control the depth of the hooks when fishing for YFT? a. Yes/no b. Generally, what depth range do you target? c. How do you keep the hooks at that depth? d. Why do you choose the depths you target? Do you think this depth is important to Yellowfin?

25. Do you have any thoughts on what a day in the life of YFT would involve? a. yes/no b. please explain

26. Is it possible to predict the numbers and types of fish there will be in a set? a. Yes/no b. Can you? c. Have you heard of any one that can << This question is to be removed – it was a bit of a “red Herring” – designed to gauge the “demeanor and honesty” of the fisher.>>

27. In your opinion, has there been any change in the abundance of yellowfin tuna since you started fishing? (circle one)

More / Less / No change / Variable/ No opinion

Catch History, Vessel Operations and Planning The next section of the interview is related to the type of planning decisions you make in relation to the business of longline fishing. The questions relate planning different amounts of time; a whole year, sections of a year (3 month blocks/seasons), a single trip, and a single set.

147 Appendix

Trip Planning

28. When deciding whether to leave the wharf, you have the following options a. Fishing where you have equal chance of making a 20% profit or 20% loss b. Fishing where you are sure to meet your expenses, but little more c. Fishing in an area where you have a equal chance making a huge profit or catching very little d. Not leaving the wharf

Please rank the options (1-4) based on the way you prefer to fish.

Would your preference change through the year? Explain

29. Does the way you plan your fishing change from trip to trip? a. Yes/no b. Is this based on the water and the weather or on the fish?

30. Do you remember your best fishing trip for YFT? a. yes/no b. When and where was it and what made it memorable?

31. Do you remember your worst trip for YFT? a. yes/no b. When and where was it and what made it memorable?

Trip year Where, when, what time Why best

worst

32. Do you ever work with other boats when fishing for YFT? a. Yes/no b. Generally how many other skippers would you work during a fishing trip? c. Do you always work with the same ones? Yes/no

33. Do you swap information about the catch from your last set with other skippers? a. Yes/no b. Overall, how accurate do you think the information coming back to you is?

(Choose value between 0 % and 100 %) 0 10 20 30 40 50 60 70 80 90 100

148 Appendix

34. When you are looking for good water to set your gear, does the catch information from other boats, or your co-operative, influence where you decide to fish? [use the following as a guide] a. ignore it – I fish where I want b. 1-20% - I try and fish away from everyone else and find my own water c. 21-40% - Take a bit of notice d. 41-60% - Half the time I‟ll fish water that is known to have “product” in it. e. 61-80% - More often than not I‟ll fish where other skippers have been getting good fish. f. 81-99% - It‟s probably one of the best ways to find good water g. 100% - Always go where I am told by the license holder/company manager/fleet manager

35. Which of the following factors do you feel are important when planning a trip? Please add you own factors if missing from the list below [give to fishers to fill out]

Fishers list always often rarely never

The last set Catch from previous seasons/years Depth Available feed/feed concentration Season Moon phase Local winds Synoptic weather Last set Barometric pressure EAC activity/local current activity Cloud cover Water colour Sea Surface Temperature Fleet dynamics Social/personal factors Economic factors

36. Are there any specific places within the ETBF fishing grounds that you target more than all others? a. yes/no b. Why do you check them? c. Did you find them yourself or where you shown them?

149 Appendix

Planning a set

37. Can you remember your best set in your fishing career since 1996? a. yes/no b. when was the best set you can remember and why was it so good c. was there any thing distinctive about the environment (the weather, water, feed)

38. Can you remember the worst set in your fishing career? a. Yes/no b. When was the worst set you can remember c. was there any thing distinctive about the environment (the weather, water, feed)

Set Year Where Why Best

Worst

39. What factors do you feel are important when planning a set? [give to fishers to fill out] Please add you own factors if missing from the list below? Fishers list always often rarely Never

Depth Available feed/feed concentration Time of Day Last set Season Moon phase Local winds Synoptic weather Catch from previous seasons/years Barometric pressure EAC activity/local current activity Cloud cover Water colour Sea Surface Temperature Social/personal factors Economic factors

150 Appendix

Seasonal Planning [Summer, Autumn, Winter, Spring]

40. Can you plan large sections of a fishing year [half year, three month sections/seasons? a. yes/no b. how do you break up the year

41. Do you remember the best and worst season/month in your fishing career? When and where was it and what made it memorable?

Seasonal year Where Why best

worst

42. Which of the factors listed below do you feel are important when planning a large section (three months) of the fishing year ? Please add you own factors if missing from the list below [give to fishers to fill out]

Fishers list always often rarely Never

Depth Available feed/feed concentration Season Moon phase Seasonal winds seasonal weather Regional/global weather EAC activity/local current activity Catch from previous seasons/years Fleet dynamics Ecology Social/personal factors Economic factors

43. At this time scale, can you predict whether there will be a good or a bad period for fishing for yellowfin tuna? a. Yes/no b. What signs do you look for in the environment?

44. In your region, do YFT behave differently at different times of the year ? a. Yes/no b. Explain

151 Appendix

45. Do YFT behave differently in different parts of the ETBF (Eastern Tuna and Bill Fish fishing zone)? a. Yes/no b. Explain (ie Active in different depths, susceptible to different baits)

Annual Planning

46. In the business of longline fishing can you have a yearly plan? a. Yes/no b. What is the most important basis of the plan. c. Do you have a five year plan? Yes/no

47. a. Do you remember the best year for YFT? Yes/no b. Do you remember the worst YFT? Yes/no c. [When was it and what made it memorable?]

Annual year Where Why Best

worst

48. What do you think controls whether there will be a good or a bad year for yellowfin? a. Yes/no b. What signs do you look for?

49. Do you think there is a yearly pattern in the availability of YFT? a. Yes/no b. What controls the pattern[ prompts -feeding, behaviour or movement]

50. On your Boat […] during you last season did you use any of the electronic devices listed below [give to fishers to fill out] a. Depth Sounder yes/no b. AutoPilot yes/no c. Sea Surface Temperature sensor yes/no d. Radio Beacons and Direction finder yes/no e. OrbBuoy/smart buoy yes/no f. Radar yes/no g. Doppler current meter yes/no h. Bathythermograph (BTG) yes/no i. GPS yes/no

152 Appendix

j. Video Plotter (mapping) yes/no k. Cellular phone yes/no l. Immarsat (ship to ship/ship to shore) yes/no m. Weather fax yes/no n. CSIRO satellite data (SST charts etc) yes/no o. Other “brands”of satellite data(which ones?) yes/no p. Laptop computer yes/no q. Internet (HF or satellite connection) yes/no r. Other specialized electronics yes/no

51. Have you added any electrical equipment since 2004? a. Yes/no b. Which devices are the most recent additions?

52. Have you made any major modifications to your fishing equipment since 2004? a. yes/no b. What have you done c. why

Discussion and Final Questions

53. Part of this research is interested in determining the gaps in the scientific knowledge relating to YFT ecology, does the science the ocean and YFT interest you? a. Yes/No b. Do you actively search for information on the fish you target (YFT, BET, SWO,SBT)

54. In your opinion, how much do we (scientists and fishers together) know about the YFT in regards to where and how they prefer to move and feed? (Choose value between 0 % and 100 %)

0 10 20 30 40 50 60 70 80 90 100

55. There are a number of terms and features that have been suggested to be important to yellowfin tuna, can you please circle the terms you think are important when looking for YFT. a. Cold core Eddy b. Warm core Eddy c. Shelf upwelling d. Taylor column e. Tropical convergence f. Tasman front g. Emergent scattering layer (feed layer) h. Shelf break i. Rossby waves j. Thermocline

153 Appendix

k. Salinity/Halocline l. Pycnocline m. Coastal upwelling n. Chlorophyll A o. Southern Oscillation Index p. La Nina q. Lord Howe Rise

56. Are there any other ocean, weather or biological features from this region that you think that are also important to the presence of YFT.

“Before I head off, do you mind if I ask a couple more questions that will help narrow down the skippers I should approach for a chat? With so many boats active in the fishery, and limited time to contact the skippers, I‟d prefer to try and interview the best fisher as recommended by their peers.”

57. On that note, do you think other fishers would answer the questions in this interview as openly as you did?

1 2 3

58. Of the fishers you know, who are the three most knowledgeable?

1 2 3

59. Who would you rate as the three most successful and/or best fishers?

1 2 3

60. Are there any fishers that you can recommend I speak to? Would you be happy to contact them on my behalf and pass on one of these information sheets?

“That‟s great, thanks very much! The information you have provided is sure to be very useful. At this stage of the interview it is worth mentioning that I plan to review, and then, repeat this interview process in a couple of months. I hope to foster an open and friendly association with the participants of this interview series and perhaps, if you are happy to, ask you to add further comment to the information you‟ve provide me today. Are there any questions or feed back you have regarding this research that you would like to put forward now? On that note, over the next few days/weeks, if you have any more thoughts regarding any of the questions or you‟d like to change or withdraw any of your answers, or perhaps you think of a few things that I might like to know, please don‟t hesitate to get in touch. Thanks again, it was really good to meet you and I really appreciated you sharing your knowledge with me.”

154 Appendix

Table 1 – Interview questions used in the cluster analysis of ETBF skippers. Numerical codes for binary responses are; 0 = no, 1 = yes. In categorical response sets zeros have been used to indicate different values in different response sets. In question 5 a zero indicates that the fisherman chooses to fish on the cold side of a temperature break. In question sets 9 and 10 a zero represents an indifferent, dismissive or evasive answer

155 Appendix

Compilation of selected interview responses

156 Appendix

Clustering ETBF Fishermen based on their opinion on optimum longline fishing scenarios for capturing YFT.

T1 - Response Data (categorical)

Skipper 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

sst 1 1 4 1 1 1 1 3 1 3 4 3 3 2 2 3 2 2 4 4 3 3 3

minbreak 0 0 0 0 0 0.5 0 0.2 0 0.05 0.5 0 0.3 0.4 0.2 0.5 0 0.1 0.3 0.3 0.5 0.3 0.2

maxbreak 0 0 0 0 0 0 0 0.5 0 0 1 4 0 0 0 1 0 0 1 2 0 2 0 break important 0 0 0 0 0 1 0 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1

sidebreak 1 1 1 1 1 1 1 2 1 1 1 2 1 0 1 1 1 1 1 1 1 2 1

current 1 1 1 1 1 3 1 3 1 3 4 2 3 3 2 3 1 2 1 4 2 3 2 bathymetry Rats 0 0 1 0 0 1 1 1 1 1 1 1 1 0 1 0 1 0 1 1 1 1 1 bathymetry Big Fish 0 0 0 0 0 0 1 1 1 1 0 1 1 0 1 0 1 0 1 1 1 0 1 Target depth Big Fish 0 0 1 0 0 0 0 0 0 0 2 0 4 4 0 3 0 2 2 2 2 2 0 Target depth Rats 0 0 1 0 0 0 0 0 0 0 1 0 2 4 0 1 1 1 1 0 0 0 0

bait 3 3 3 3 3 3 3 3 2 2 3 1 3 2 2 1 1 1 1 1 1 2 1 soak 2 2 2 2 2 2 3 2 3 2 1 5 2 1 2 2 2 3 5 4 2 2 4

Note : not all the observations and variables were used in the Cluster analysis – There are a mix of binary, numeric, categorical and missing values Missing values are not accepted in the Bray-Curtis or the Euclidian Distance measures used to create the Resemblance matrix and have to be estimated using the EM algorithim. This required some variables and samples (skippers) to be removed from the dataset prior to normalization. The data formats restricted us to using a Modified Gower algorithm to construct the resemblance matrix for the cluster analysis Below are the summaries of the normalized variables included in the PRIMER cluster analysis

157 Appendix

Statistical details and results

Normalise Normalise variables

Data worksheet Name: Data1 Data type: Other Sample selection: All Variable selection: All

T2 Variable Mean SD SST 2.3913 1.1176 minbreak 0.18913 0.19421 maxbreak 0.5 0.98857 breakimport 0.65217 0.48698 Sidebreak 1.087 0.41703 Current 2.087 1.0407 BathymetryRats 0.69565 0.47047 BathymetryBigfish 0.52174 0.51075 TDBigfish 1.0435 1.3644 TDRats 0.52174 0.94722 Bait 2.087 0.90015 Soak 2.4783 1.0816

These data were then prepared as a resemblance matrix Resemblance Create lower triangular resemblance matrix

Data worksheet Name: Data2 Data type: Other Sample selection: All Variable selection: All

Parameters Analyse between: Samples Resemblance measure: Modified Gower Log base: 10

Outputs Worksheet: Resem1 1 2 3 4 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 0 3 0.789548 0.789548 4 0 0 0.789548 6 0 0 0.789548 0 7 0.840861 0.840861 0.766071 0.840861 0.840861 8 0.666667 0.666667 0.631638 0.666667 0.666667 0.700718 9 0.905761 0.905761 0.687314 0.905761 0.905761 0.568079 0.620046 10 1.002058 1.002058 0.832873 1.002058 1.002058 0.868414 0.335392 0.763786 11 1.001029 1.001029 0.737766 1.001029 1.001029 0.601497 0.667696 0.335214 0.6 12 0.958545 0.958545 0.607635 0.958545 0.958545 0.4914 0.862691 0.633385 0.963308 0.663308 13 1.157897 1.157897 0.935707 1.157897 1.157897 0.946748 0.907897 0.546983 0.893857 0.657125 0.824174 14 0.947681 0.947681 0.631924 0.947681 0.947681 0.525937 0.725459 0.486945 0.837256 0.503923 0.315604 0.816025 15 1.15711 1.15711 1.012606 1.15711 1.15711 0.725068 1.117832 0.928298 1.133783 0.86706 0.610898 1.108499 0.490392 16 1.001544 1.001544 0.860728 1.001544 1.001544 0.701747 0.501544 0.620929 0.333333 0.4 0.863308 0.751 0.726145 0.990926 17 1.045877 1.045877 0.836134 1.045877 1.045877 0.684745 1.036702 0.791577 1.040094 0.70676 0.287257 0.874349 0.458308 0.41611 0.928982 18 1.001544 1.001544 0.632873 1.001544 1.001544 0.887212 0.501544 0.793313 0.333333 0.666667 0.863308 0.907125 0.726145 0.990926 0.5 0.928982 19 1.001544 1.001544 0.860728 1.001544 1.001544 0.887212 1.001029 0.927389 1 0.857143 0.737009 1.028556 0.726145 0.587297 0.8 0.622977 0.8 20 1.053198 1.053198 0.737379 1.053198 1.053198 0.794208 0.853198 0.739359 0.836201 0.652581 0.440228 0.504758 0.460101 0.746229 0.725089 0.488665 0.725089 0.725089 21 1.075811 1.075811 0.87272 1.075811 1.075811 0.696242 0.875811 0.645313 0.861326 0.527993 0.412711 0.535026 0.480658 0.768842 0.750215 0.511278 0.875194 0.875194 0.266485 22 1.030069 1.030069 0.796084 1.030069 1.030069 0.625772 0.744355 0.616756 0.700718 0.457758 0.542878 0.717923 0.415512 0.758663 0.534051 0.572948 0.743472 0.743472 0.414457 0.439583 23 1.05862 1.05862 0.868577 1.05862 1.05862 0.636877 0.952758 0.518726 0.946823 0.61349 0.415449 0.573559 0.55061 0.717544 0.835712 0.431361 0.952141 0.835712 0.549747 0.391118 0.52508 24 1.025738 1.025738 0.758946 1.025738 1.025738 0.794842 0.692405 0.561844 0.629651 0.358042 0.798303 0.444106 0.668356 1.009415 0.429651 0.85091 0.691376 0.878322 0.486395 0.51152 0.478937 0.766966

158 Appendix

Spatial Investigation of fishers’ activity 2000-2004 The logbook records of the interviewed skippers was examined and all longline locations between 2000 and 2004 were used to define area each fisher spent 95% of their fishing effort. This area was defined by 6 metrics. The Mean Longitude and Latitude of all sets, and the 97.5 and 2.25 percentiles for both Lon and Lat. This effectively defined the “box” of the Tasman Sea that was fished by each skipper. The multivariate analyses below are investigations of these spatial data.

CLUSTER Hierarchical Cluster analysis

Parameters Cluster mode: Complete linkage

Simprof test

Data worksheet Name: Data2 Data type: Other Sample selection: All Variable selection: All

Simprof Parameters Permutations for mean profile: 1000 Simulation permutations: 999 Significance level: 5% Resemblance: Analyse between: Samples Resemblance measure: D1 Euclidean distance

Analyzing performance measures of the Interviewed skippers.

Simple metrics were used to rate fishers performance during 2000-2004. These metrics were; total fish caught (of all species), nominal catch rate of all fish, nominal catch rate of YFT, and proportion of YFT catch.

CLUSTER Hierarchical Cluster analysis

Resemblance worksheet Name: Resem8 Data type: Distance Selection: All

Parameters Cluster mode: Complete linkage

Simprof test

Data worksheet Name: Data5

159 Appendix

Data type: Other Sample selection: All Variable selection: All

Simprof Parameters Permutations for mean profile: 1000 Simulation permutations: 999 Significance level: 5% Resemblance: Analyse between: Samples Resemblance measure: D1 Euclidean distance

ANOVA Metrics of catch efficiency vs Interview clusters. > catchEffvIntClust Analysis of Variance Table

Response: QnSkipPerformSpace$CatchEff0004 D Sum Sq Mean F Pr(> f Sq valu F) e as.factor(QnSkipPerformSpace$InterVi 2 0005588 0.00027 1.97 0.16 ewCluster) 0 1 940 26 80 Residuals 1 0.00254 0.00014 8 958 164

Residuals vs Fitted Normal Q-Q

9 9

18 2 18

0.02

1

0.01

0

0.00

Residuals

Standardized residuals Standardized -1

14

14 -0.02

0.020 0.025 0.030 0.035 -2 -1 0 1 2

Fitted values Theoretical Quantiles

Scale-Location Residuals vs Leverage 189 1 9 2 18

14 s

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Standardized residuals Standardized 15

Cook's distance 0.5

0.0 -2

0.020 0.025 0.030 0.035 0.00 0.10 0.20 0.30

Fitted values Leverage

ANOVA metrics of YFTcatchEff v Interview Cluster Response: QnSkipPerformSpace$YFTCatchEff0004 Sum Sq Mean F Pr(>F D Sq valu ) f e

160 Appendix as.factor(QnSkipPerformSpace$InterV 2 0.00023 0.00011 5.70 0.012 iewCluster) 502 751 61 04 * Residuals 0.00037 0.00002 1 069 059 8

Signif. codes: 0 „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟ 1

Residuals vs Fitted Normal Q-Q

0.010 2

97 9 7

1

0.005

0

0.000

Residuals

-1

Standardized residuals Standardized -0.005

17 17

0.008 0.012 0.016 -2 -1 0 1 2

Fitted values Theoretical Quantiles

Scale-Location Residuals vs Leverage 97 17 2 97

0.5

s

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0.5

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0.008 0.012 0.016 0.00 0.10 0.20 0.30

Fitted values Leverage

ANOVA of YFT Proportion metric vs Interview Cluster Response: QnSkipPerformSpace$YFTprop0004 Sum Sq Mean F Pr(> Df Sq value F) as.factor(QnSkipPerformSpace$InterViewC 2 0.0482 0.0241 2.0145 0.16 luster) 89 45 24 Residuals 18 0.2157 0.0119 37 85 No significance in the relationship between YFT catch proportion and interview clusters

161 Appendix

Residuals vs Fitted Normal Q-Q

1

0.1

0.0 0

Residuals

-0.1

-1 Standardized residuals Standardized 18 19

-0.2 18 17 19

-2 17

0.35 0.40 0.45 0.50 -2 -1 0 1 2

Fitted values Theoretical Quantiles

Scale-Location Residuals vs Leverage 17 19 0.5

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s

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0.0 1

0.35 0.40 0.45 0.50 0.00 0.10 0.20 0.30

Fitted values Leverage

ANOVA of catch Efficiency metrics (all species) vs Spatial Clusters Response: QnSkipPerformSpace$CatchEff0004 D Sum Sq Mean Sq F Pr(> f valu F) e as.factor(QnSkipPerformSpace$EUCspat 2 0.000005 0.000002 0.01 0.98 ialClust) 54 77 61 4 Residuals 1 0.003102 0.000172 8 85 38

162 Appendix

Residuals vs Fitted Normal Q-Q

9 9

18 2 18

0.02

1

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0.00

Residuals

Standardized residuals Standardized -1

15 -0.02 15

0.0274 0.0278 0.0282 0.0286 -2 -1 0 1 2

Fitted values Theoretical Quantiles

Scale-Location Residuals vs Leverage

1.5 9 18 0.5 9

15 2 18

s

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a

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Cook's distance 15

-2 0.0

0.0274 0.0278 0.0282 0.0286 0.00 0.05 0.10 0.15 0.20

Fitted values Leverage

ANOVA of YFTcatchEfficiency vs Spatial Clusters Analysis of Variance Table

Response: QnSkipPerformSpace$YFTCatchEff0004 Sum Sq Mean Sq F Pr(> D valu F) f e as.factor(QnSkipPerformSpace$EUCspat 2 0.000096 0.000048 1.69 0.21 ialClust) 01 00 52 16 Residuals 1 0.000509 0.000028 8 71 32 Not a significant linear relationship between YFT catch efficiency and spatial clusters

ANOVA of YFT Proportion of total catch vs Spatial Clusters Response: QnSkipPerformSpace$YFTprop0004 Sum Sq Mean F Pr(>F) D Sq valu f e as.factor(QnSkipPerformSpace$EUCspat 2 0.1457 0.0728 11.0 0.00072 ialClust) 73 87 95 52 *** Residuals 0.1182 0.0065 1 53 70 8

Signif. codes: 0 „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟ 1

163 Appendix

Residuals vs Fitted Normal Q-Q

16 20 20 16

1

0.05

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Residuals

-0.05

-1 Standardized residuals Standardized

6

-0.15 6 -2

0.25 0.30 0.35 0.40 0.45 -2 -1 0 1 2

Fitted values Theoretical Quantiles

Scale-Location Residuals vs Leverage 6 20 16

16 20

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Fitted values Leverage

Summary table showing the quantiles of the distribution of fishing locations for each fisher interviewed and the corresponding clusters and PCA scores used to characterise them. NOTE Quantiles of fishing locations can be plotted on Maps of Tasman sea, (see figures document)

Fisher ID LATquant.2.5%LATquant.50% LATquant.97.5%LONquant.2.5%LONquant.50%LONquant.97.5%EUCspatialClustEUCspatialPCA1 EUCspatialPCA2EUCspatialPCA3EUCperformClustEUCperformPCA1EUCperformPCA2EUCperformPCA3InterViewCluster 1 -36.0613 -34.35 -26.7013 150.635 151.975 155.5325 1 1.62 0.116 -0.396 3 -1.49 -1.4 -0.715 1 2 -37.069 -34.92 -31.6725 150.481 151.65 153.53 1 2.64 -0.138 0.383 3 -2 -0.0387 0.348 1 3 -37.7283 -35.8 -31.8925 150.3645 151.15 153.8438 1 2.95 0.0173 0.379 3 -0.877 -0.759 0.475 3 5 -36.8705 -36 -31.827 150.33 150.92 153.5535 1 2.93 -0.221 0.338 3 -1.38 0.232 0.597 1 6 -35.02 -28.87 -24.8 151.3912 154.02 156.8475 2 -0.0559 0.0697 -0.547 3 -0.0207 -0.767 0.488 3 7 -37.0545 -34 -29.4955 150.38 152.45 154.1215 1 2.13 0.15 0.0306 1 1.85 -0.469 -0.711 2 8 -35.5468 -32.7 -28.9563 151.03 153.24 156.9838 1 1.15 0.115 0.318 3 -1.98 -0.06 0.218 3 9 -37.0288 -33.46 -25.4525 150.42 152.615 155.83 1 1.43 0.543 -0.663 3 0.515 -0.641 0.292 2 10 -34.9938 -28.175 -22.1925 151.9887 154.625 160.4563 2 -1.03 0.552 -0.652 2 -1.91 0.414 -1.68 3 11 -35.065 -31.92 -27.871 151.603 153.55 158.715 1 0.502 0.152 0.356 3 -0.104 -0.41 0.329 3 13 -37.047 -35.6 -27.9245 150.403 151.25 154.2 1 2.34 0.0984 -0.34 3 0.304 0.488 0.0195 3 14 -28.8205 -26.65 -24.0485 153.749 154.13 157.3 2 -1.93 -1.84 -0.0393 3 0.143 -0.248 1.69 3 15 -30.535 -28.18 -26.93 153.75 154.22 155.8075 2 -1.11 -1.72 0.536 3 0.668 -0.343 -0.758 2 16 -36.985 -36.13 -29.895 150.3765 150.83 153.8475 1 2.72 -0.09904 -0.0142 1 1.62 -1.77 -0.281 3 17 -32.787 -26.17 -21.2613 152.3143 156 166.5217 3 -2.51 0.93 -0.305 1 2.55 -0.886 -0.703 2 18 -33.732 -29.52 -24.177 153.342 159.58 167.622 3 -2.7 1.55 1.44 2 -1.58 1.64 0.173 3 19 -35.9515 -27.67 -22.2755 151.5395 156.83 163.734 3 -1.5 1.6 -0.277 1 1.97 1.79 0.413 3 20 -33.0395 -27.03 -21.4975 154.3245 157.05 162.138 3 -2.74 0.113 0.305 2 -0.252 1.19 -2.38 3 21 -33.712 -27.6 -19.5 152.632 157.23 163.83 3 -2.49 1.04 -0.395 1 1.8 2.07 0.821 3 22 -28.6918 -26.68 -24.0355 153.67 154.18 157 2 -1.91 -1.88 -0.065 3 -0.298 -2.52 1.82 3 23 -29.7413 -26.07 -21.4615 153.6488 154.91 159.297 2 -2.45 -1.15 -0.396 3 0.514 0.217 -0.464 2

164